remove conflict

doc/design/python_api.md

+# Design Doc: Python API
+
+Due to the refactorization of the PaddlePaddle core, we need Python classes to construct corresponding protobuf messages that describe a DL program.
+
+| Python classes | Protobuf messages |
+| --- | --- |
+| Program | ProgramDesc |
+| Block | BlockDesc |
+| Operator | OpDesc |
+| Variable | VarDesc |
+
+Please be aware that these Python classes need to maintain some construction-time information, which are not part of the protobuf messages.
+
+## Core Concepts
+
+### Program
+
+A `ProgramDesc` describes a [DL program](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/program.md), which is composed of an array of `BlockDesc`s.  A `BlockDesc` refers to its parent block by its index in the array.  For example, operators in the step block of an RNN operator needs to be able to access variables in its ancessor blocks.
+
+Whenever we create a block, we need set its parent block to the current block, so the Python class `Program` needs to maintain a data member `current_block`.
+
+```python
+class Program(objects):
+    def __init__(self):
+        self.proto = core.NewProgram() # a C++ ProgramDesc pointer.
+        self.blocks = vector<Block>()
+        self.blocks.append(Block(self, -1)) # the global block
+        self.current_block = 0          # initialized to the global block
+
+    def global_block():
+        return self.blocks[0]
+
+    def current_block():
+        return self.get_block(self.current_block)
+
+    def rollback():
+        self.current_block = self.current_block().parent_idx
+
+    def create_block():
+        new_block_idx = len(self.block)
+        self.blocks.append(Block(self, self.current_block))
+        self.current_block = new_block_idx
+        return current_block()
+```
+
+`Program` is an accessor to the protobuf message `ProgramDesc`, which is created in C++ space, because the InferShape function is in C++, which manipulates `VarDesc` messages, which are in turn members of `BlockDesc`, which is a member of `ProgramDesc`.
+
+`Program` creates the first block as the global block in its constructor.  All parameters and their initializer operators are in the global block.
+
+### Block
+
+A [Block](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/block.md) includes
+
+1. a map from variable names to an instance of the Python `Variable` class, and
+1. a list of `Operator` instances.
+
+```python
+class Block(objects):
+    def __init__(self, program, parent_idx):
+        self.proto = core.NewBlock(program.proto)
+        self.program = program
+        self.vars = map<string, Variable>()
+        self.ops = vector<Operator>()
+        self.parent_idx = parent_idx
+
+    def create_var(self, ...):
+        return Variable(self, ...)
+
+    def _create_global_var(self, ...):
+        program.global_block().create_var(...)
+
+    def create_parameter(self, name, ...):
+        # Parameter is a subclass of variable. See Parameter section for details.
+        self.vars[name] = Parameter(self._create_global_var(...), ...)
+        return self.vars[name]
+
+    def append_operator(self, ...):
+        self.ops.append(Operator(self, ...))
+
+    def prepend_operator(self, ...): # Parameter's ctor prepands initialize operators.
+       self.ops.prepend(Operator(self, ...))
+```
+
+`create_parameter` is necessary because parameters are global variables, those defined in the global block, but can be created in some sub-blocks, e.g., an FC layer in the step block of an RNN operator.
+
+`prepand_operator` is necessary because the constructor of `Parameter` needs to create the initialize (or load) operator of the parameter, and would like to put it in the *preamble* of the global block.
+
+### Operator
+
+The `Operator` class fills in the `OpDesc` message and calls the C++ function `InferShape` to infer output shape from input shape.
+
+```python
+class Operator(object):
+    def __init__(self,
+                 block,  # Block
+                 type,   # string
+                 inputs, # dict<string, Variable>
+                 outputs,# dict<stirng, Variable>
+                 attrs   # dict<string, Any>
+                 ):
+        self.proto = core.NewOpDesc(block.proto, type, inputs, outputs, attrs)
+        core.infer_shape(self.proto, inputs, outputs)
+
+    def type(self):
+        return self.proto.type()
+```
+
+`Operator` creates the `OpDesc` message in C++ space, so could it call the `InferShape` function, which is in C++.
+
+### Variable
+
+Operators take Variables as its inputs and outputs.
+
+```python
+class Variable(object):
+    def __init__(self,
+                 block=None,      # Block
+                 name=None,       # string
+                 shape,           # tuple
+                 dtype="float32", # string
+                 lod_level=None   # int
+                 ):
+        if name is None:
+            name = unique_name_generator()
+        self.name = name
+        self.block = block
+        self.proto = core.NewVarDesc(block.proto, name, shape, lod_level)
+        self.writer = None
+```
+
+Please be aware of `self.writer`, that tracks operator who creates the variable.  It possible that there are more than one operators who write a variable, but in Python space, each writes to a variable is represented by a Variable class.  This is guaranteed by the fact that **`core.NewVarDesc` must NOT create a new `VarDesc` message if its name already exists in the specified block**.
+
+### Parameter
+
+A parameter is a global variable with an initializer (or load) operator.
+
+```python
+class Parameter(Variable):
+    def __init__(self,
+                 block=None,      # Block
+                 name=None,       # string
+                 shape,           # tuple
+                 dtype="float32", # string
+                 lod_level=None   # int
+                 trainable,       # bool
+                 initialize_op_attrs,
+                 optimize_op_attrs):
+        super(Parameter, self).__init__(block, name, shape, dtype, lod_level)
+        self.trainable = trainable
+        self.optimize_op_attrs = optimize_op_attrs
+        block.prepend(Operator(block,  # Block
+                               initialize_op_attrs['type'],   # string
+                               None,   # no inputs
+                               self,   # output is the parameter
+                               initialize_op_attrs)
+```
+
+When users create a parameter, s/he can call
+
+```python
+program.create_parameter(
+  ...,
+  init_attr={
+    type: "uniform_random",
+    min: -1.0,
+    max: 1.0,
+  })
+)
+```
+
+In above example, `init_attr.type` names an initialize operator.  It can also name the load operator
+
+```python
+init_attr={
+ type: "load",
+ filename: "something.numpy",
+}
+```
+
+`optimize_op_attrs` is not in the `VarDesc` message, but kept in the Python instance, as it will be used in the Python space when creating the optimize operator's `OpDesc`, and will be in the `OpDesc` message.
+
+## Layer Functions
+
+A layer is a Python function that creates some operators and variables.  Layers simplify the work of application programmers.
+
+### Data Layer
+
+```python
+def data_layer(name, type, column_name):
+    block = the_current_program.glolal_block()
+    var = block.create_global_var(
+            name=name,
+            shape=[None] + type.dims(),
+            dtype=type.dtype)
+    block.prepend_operator(block,
+                           type="Feed",
+                           inputs = None,
+                           outputs = [var],
+                           {column_name: column_name})
+    return var
+```
+
+The input to the feed operator is a special variable in the global scope, which is the output of [Python readers](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/reader/README.md).
+
+### FC Layer
+
+```python
+def fc_layer(input, size, ...):
+    block = program.current_block()
+    w = block.create_parameter(...)
+    b = block.create_parameter(...)
+    out = block.create_var()
+    op = block.append_operator("FC", X=input, W=w, b=b, out=out)
+    out.writer = op
+    return out
+```

doc/design/refactorization.md

 # Design Doc: Refactorization Overview
 
-The goal of refactorizaiton include:
+The goals of refactoring include:
 
-1. Make it easy for external contributors to write new elementory computaiton operations.
-1. Make the codebase clean and readable.
-1. Introduce a new design of computation representation -- a computation graph of operators and variables.
-1. The graph representation helps implementing auto-scalable and auto fault recoverable distributed computing.
+1. Making it easy for external contributors to write new elementary computation operations.
+1. Making the codebase clean and readable.
+1. Designing a new computation representation -- a computation graph of operators and variables.
+1. Implementing auto-scalability and auto fault recoverable distributed computing with the help of computation graphs.
 
 ## Computation Graphs
 
-1. PaddlePaddle represent the computation, training and inference of DL models, by computation graphs.
+1. PaddlePaddle represents the computation, training and inference of Deep Learning models, by computation graphs.
 
-  1. Please dig into [computation graphs](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/graph.md) for a solid example.
+  1. Please refer to [computation graphs](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/graph.md) for a concrete example.
 
-1. Users write Python programs to describe the graphs and run it (locally or remotely).
+1. Users write Python programs to describe the graphs and run them (locally or remotely).
 
 1. A graph is composed of *variables* and *operators*.
 
-1. The description of graphs must be able to be serialized/deserialized, so it
+1. The description of graphs must be capable of being serialized/deserialized, so that:
 
-   1. could to be sent to the cloud for distributed execution, and
-   1. be sent to clients for mobile or enterprise deployment.
+   1. It can to be sent to the cloud for distributed execution, and
+   1. It can be sent to clients for mobile or enterprise deployment.
 
-1. The Python program do
+1. The Python program does the following steps
 
-   1. *compilation*: runs a Python program to generate a protobuf message representation of the graph and send it to
+   1. *compilation*: run a Python program to generate a protobuf message representation of the graph and send it to
       1. the C++ library `libpaddle.so` for local execution,
       1. the master process of a distributed training job for training, or
       1. the server process of a Kubernetes serving job for distributed serving.
-   1. *execution*: according to the protobuf message, constructs instances of class `Variable` and `OperatorBase`, and run them.
+   1. *execution*: execute the graph by constructing instances of class [`Variable`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/variable.h#L24) and [`OperatorBase`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/operator.h#L70), according to the protobuf message.
 
-## Description and Realization
+## Description and Realization of Computation Graph
 
-At compile time, the Python program generates protobuf message representation of the graph, or the description of the graph.
+At compile time, the Python program generates a protobuf message representation of the graph, or the description of the graph.
 
-At runtime, the C++ program realizes the graph and run it.
+At runtime, the C++ program realizes the graph and runs it.
 
 | | Representation (protobuf messages) | Realization (C++ class objects) |
 |---|---|---|
@@ -42,30 +42,31 @@ At runtime, the C++ program realizes the graph and run it.
 |Operation|[OpDesc](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L35)|[Operator](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/operator.h#L64)|
 |Block|BlockDesc|Block|
 
-The word *graph* is exchangable with *block* in this document.  A graph represent computation steps and local variables as a C++/Java program block, or a pair of { and }.
+The word *graph* is interchangeable with *block* in this document.  A graph represents computation steps and local variables similar to a C++/Java program block, or a pair of parentheses(`{` and `}`).
 
 ## Compilation and Execution
 
-1. Run an applicaton Python program to describe the graph.  In particular,
+1. Run an application Python program to describe the graph.  In particular, the Python application program does the following:
 
-   1. create VarDesc to represent local/intermediate variables,
-   1. create operators and set attributes,
-   1. validate attribute values,
-   1. inference the type and the shape of variables,
-   1. plan for memory-reuse for variables,
-   1. generate backward and optimization part of the Graph.
-   1. possiblly split the graph for distributed training.
+   1. Create `VarDesc` to represent local/intermediate variables,
+   1. Create operators and set attributes,
+   1. Validate attribute values,
+   1. Infer the type and the shape of variables,
+   1. Plan memory-reuse for variables,
+   1. Generate the backward graph
+   1. Optimize the computation graph.
+   1. Potentially, split the graph for distributed training.
 
-1. The invocation of `train` or `infer` in the application Python program:
+1. The invocation of `train` or [`infer`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/inference.py#L108) methods in the application Python program does the following:
 
-   1. create a new Scope instance in the [scope hierarchy](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/scope.md) for each run of a block,
+   1. Create a new Scope instance in the [scope hierarchy](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/scope.md) for each run of a block,
       1. realize local variables defined in the BlockDesc message in the new scope,
       1. a scope is similar to the stack frame in programming languages,
 
-   1. create an instance of class `Block`, in which,
+   1. Create an instance of class `Block`, in which,
       1. realize operators in the BlockDesc message,
 
-   1. run the Block by calling
+   1. Run the Block by calling
       1. `Block::Eval(vector<Variable>* targets)` for forward and backward computations, or
       1. `Block::Eval(vector<Operator>* targets)` for optimization.
 
@@ -76,14 +77,14 @@ The word *graph* is exchangable with *block* in this document.  A graph represen
 Compile Time -> IR -> Runtime
 ```
 
-### Benefit
+### Benefits of IR
 
 - Optimization
   ```text
   Compile Time -> IR -> Optimized IR -> Runtime
   ```
-- Send automatically partitioned IR to different nodes.
-  - Automatic data parallel
+- Automatically send partitioned IR to different nodes.
+  - Automatic Data Parallelism
     ```text
     Compile Time
     |-> Single GPU IR
@@ -92,7 +93,7 @@ Compile Time -> IR -> Runtime
             |-> Node-1 (runs trainer-IR-1)
             |-> Node-2 (runs pserver-IR)
     ```
-  - Automatic model parallel (planned for future)
+  - Automatic Model Parallelism (planned for future)
 
 ---
 
@@ -105,10 +106,10 @@ Compile Time -> IR -> Runtime
 # Operator
 ![class_diagram](http://api.paddlepaddle.org/graphviz?dot=https://gist.githubusercontent.com/reyoung/53df507f6749762675dff3e7ce53372f/raw/dd598e8f1976f5759f58af5e5ef94738a6b2e661/op.dot)
 
-* `Operator` is the fundamental building block as the user interface.
-    * Operator stores input/output variable name, and attributes.
-    * The `InferShape` interface is used to infer output variable shapes by its input shapes.
-    * Use `Run` to compute `input variables` to `output variables`.
+* `Operator` is the fundamental building block of the user interface.
+    * Operator stores input/output variable names, and attributes.
+    * The `InferShape` interface is used to infer the shape of the output variable shapes based on the shapes of the input variables.
+    * Use `Run` to compute the `output` variables from the `input` variables.
 
 ---
 
@@ -126,30 +127,29 @@ Compile Time -> IR -> Runtime
 # Why separate Kernel and Operator
 
 * Separate GPU and CPU code.
-    * Make Paddle can run without GPU.
-* Make one operator (which is user interface) can contain many implementations.
-    * Same mul op, different FP16, FP32 Kernel. different MKL, eigen kernel.
+    * Make Paddle capable of running without GPU.
+* Make one operator (which is a user interface) and create many implementations.
+    * For example, same multiplication op can have different implementations kernels such as FP16 kernel, FP32 kernel, MKL, eigen kernel.
 ---
 
 # Libraries for Kernel development
 
 * `Eigen::Tensor` contains basic math and element-wise functions.
     * Note that `Eigen::Tensor` has broadcast implementation.
-    * Limit number of `tensor.device(dev) = ` in your code.
-* `thrust::tranform` and `std::transform`.
-    * `thrust` has the same API as C++ standard library. Using `transform` can quickly implement a customized elementwise kernel.
-    * `thrust` has more complex API, like `scan`, `reduce`, `reduce_by_key`.
+    * Limit the number of `tensor.device(dev) = ` in your code.
+* `thrust::transform` and `std::transform`.
+    * `thrust` has the same API as C++ standard library. Using `transform`, one can quickly implement customized element-wise kernels.
+    * `thrust` also has more complex APIs, like `scan`, `reduce`, `reduce_by_key`.
 * Hand-writing `GPUKernel` and `CPU` code
-    * Do not write `.h`. CPU Kernel should be in `.cc`. GPU kernel should be in `.cu`. (`GCC` cannot compile GPU code.)
+    * Do not write in header (`.h`) files. CPU Kernel should be in cpp source (`.cc`) and GPU kernels should be in cuda (`.cu`) files. (GCC cannot compile GPU code.)
 ---
-# Operator Register
+# Operator Registration
 
-## Why register is necessary?
+## Why is registration necessary?
 We need a method to build mappings between Op type names and Op classes.
 
-## How to do the register?
-
-Maintain a map, whose key is the type name and value is corresponding Op constructor.
+## How is registration implemented?
+Maintaining a map, whose key is the type name and the value is the corresponding Op constructor.
 
 ---
 # The Registry Map
@@ -169,7 +169,7 @@ Maintain a map, whose key is the type name and value is corresponding Op constru
 # Related Concepts
 
 ### Op_Maker
-It's constructor takes `proto` and `checker`. They are compeleted during Op_Maker's construction. ([ScaleOpMaker](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37))
+It's constructor takes `proto` and `checker`. They are completed during Op_Maker's construction. ([ScaleOpMaker](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37))
 
 ### Register Macros
 ```cpp
@@ -177,34 +177,34 @@ 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.
+### USE Macros
+Make sure the registration process is executed and linked.
 
 ---
-# Register Process
-1. Write Op class, as well as its gradient Op class if there is.
-2. Write Op maker class. In the constructor, describe its inputs, outputs, and attributes.
-3. Invoke macro `REGISTER_OP`. The macro will
-	1. call maker class to complete `proto` and `checker`
-	2. with the completed `proto` and `checker`, build a new key-value pair in the `OpInfoMap`
+# Registration Process
+1. Write an Op class and its gradient Op class, if required.
+2. Write an Op maker class. In the constructor of this class, describe the inputs, outputs and attributes of the operator.
+3. Invoke the macro `REGISTER_OP`. This macro will
+	1. Call maker class to complete the `proto` and the `checker`
+	2. Using the completed `proto` and `checker`, it will add a new key-value pair to the `OpInfoMap`
 
-4. Invoke `USE` macro in where the Op is used to make sure it is linked.
+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
-- Mapping from forwarding Op to backward Op
+- Mapping from forward Op to backward Op
 ![backward](https://gist.githubusercontent.com/dzhwinter/a6fbd4623ee76c459f7f94591fd1abf0/raw/61026ab6e518e66bde66a889bc42557a1fccff33/backward.png)
 
 ---
 # Backward Module (2/2)
 ### Build Backward Network
-- **Input** graph of forwarding operators
-- **Output** graph of backward operators
-- **corner case in construction**
-	- shared variable => insert `Add` operator
-	- no gradient => insert `fill_zero_grad` operator
-	- recursive netOp => call `Backward` recursively
+- **Input**: graph of forward operators
+- **Output**: graph of backward operators
+- **Corner cases in construction**
+	- Shared Variables => insert an `Add` operator to combine gradients
+	- No Gradient => insert a `fill_zero_grad` operator
+	- Recursive NetOp => call `Backward` recursively
 	- RNN Op => recursively call `Backward` on stepnet
 
 
@@ -213,41 +213,41 @@ make sure the registration process is executed and linked.
 
 * `Tensor` is an n-dimension array with type.
 	* Only dims and data pointers are stored in `Tensor`.
-	* All operators on `Tensor` is written in `Operator` or global functions.
-	* variable length Tensor design [LoDTensor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md)
-* `Variable` is the inputs and outputs of an operator. Not just `Tensor`.
-	* step_scopes in RNN is a variable and not a tensor.
-* `Scope` is where variables store at.
-	* map<string/*var name */, Variable>
-	* `Scope` has a hierarchical structure. The local scope can get variable from its parent scope.
+	* All operations on `Tensor` are written in `Operator` or global functions.
+	* Variable length Tensor design [LoDTensor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md)
+* `Variable` instances are the inputs and the outputs of an operator. Not just `Tensor`.
+	* `step_scopes` in RNN is a variable and not a tensor.
+* `Scope` is where variables are stores.
+	* map<string `variable_name`, Variable>
+	* `Scope` has a hierarchical structure. The local scope can get variables from its parent scope.
 
 ---
 # Block (in design)
-## the difference with original RNNOp
-- as an operator is more intuitive than `RNNOp`,
-- offers new interface `Eval(targets)` to deduce the minimal block to `Run`,
-- fits the compile-time/ runtime separation design.
-  - during the compilation, `SymbolTable` stores `VarDesc`s and `OpDesc`s and serialize to a `BlockDesc`
-  - when graph executes, a Block with `BlockDesc` passed in creates `Op` and `Var` then `Run`
+## the difference between original RNNOp and Block
+- As an operator is more intuitive than `RNNOp`,
+- Offers a new interface `Eval(targets)` to deduce the minimal block to `Run`,
+- Fits the compile-time/ runtime separation design paradigm.
+  - During the compilation, `SymbolTable` stores `VarDesc`s and `OpDesc`s and serialize to a `BlockDesc`
+  - When graph executes, a Block with `BlockDesc` is passed. It then creates `Op` and `Var` instances and then invokes `Run`.
 
 ---
 # Milestone
-- take Paddle/books as the main line, the requirement of the models motivates framework refactoring,
-- model migration
-  - framework development gives **priority support** to model migration, for example,
+- Take Paddle/books as the main line, the requirement of the models motivates framework refactoring,
+- Model migration
+  - Framework development gives **priority support** to model migration, for example,
     - the MNIST demo needs a Python interface,
     - the RNN models require the framework to support `LoDTensor`.
-  - determine some timelines,
-  - heavily-relied Ops need to be migrated first,
-  - different models can be migrated parallelly.
-- improve the framework at the same time
-- accept imperfection, concentrated on solving the specific problem at the right price.
+  - Determine some timelines,
+  - Frequently used Ops need to be migrated first,
+  - Different models can be migrated in parallel.
+- Improve the framework at the same time
+- Accept imperfection, concentrate on solving the specific problem at the right price.
 
 ---
 # Control the migration quality
-- compare the performance of migrated models with old ones.
-- follow google C style
-- build the automatic workflow of generating Python/C++ documentations
-  - the documentation of layers and ops should be written inside the code
-  - take the documentation quality into account when doing PR
-  - preview the documentations, read and improve them from users' perspective
+- Compare the performance of migrated models with old ones.
+- Follow the google C++ style
+- Build the automatic workflow of generating Python/C++ documentations.
+  - The documentation of layers and ops should be written inside the code.
+  - Take the documentation quality into account when submitting pull requests.
+  - Preview the documentations, read and improve them from a user's perspective.

doc/howto/dev/new_op_cn.md

@@ -285,41 +285,27 @@ class TestMulGradOp(GradientChecker):
             'Y': np.random.random((84, 100)).astype("float32")
         }
 
-    def test_cpu_gpu_compare(self):
-        self.compare_grad(self.op, self.inputs)
-
-    def test_normal(self):
+    def test_check_grad_normal(self):
         # mul op will enlarge the relative error
-        self.check_grad(
-            self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5)
+        self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5)
 
-    def test_ignore_x(self):
+    def test_check_grad_ingore_x(self):
         self.check_grad(
-            self.op,
-            self.inputs, ["Y"],
-            "Out",
-            max_relative_error=0.5,
-            no_grad_set={"X"})
+            ['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X"))
 
-    def test_ignore_y(self):
+    def test_check_grad_ingore_y(self):
         self.check_grad(
-            self.op,
-            self.inputs, ["X"],
-            "Out",
-            max_relative_error=0.5,
-            no_grad_set={"Y"})
+            ['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
 ```
 
 下面解释代码中一些关键的地方:
 
 - 调用`create_op("mul")`创建反向Op对应的前向Op。
-- 调用`compare_grad`函数对比CPU、GPU计算结果。
-- `test_normal`中调用`check_grad`使用数值法检测梯度正确性和稳定性。
-  - 第一个参数`self.op` : 前向Op。
-  - 第二个参数`self.inputs` : 输入词典，词典的Key和`ProtoMaker`定义保持一致。
-  - 第三个参数`["X", "Y"]` : 指定对输入变量`X`、`Y`做梯度检测。
-  - 第四个参数`"Out"` : 指定前向网络最终的输出目标变量`Out`
-- `test_ignore_x`和`test_ignore_y`分支用来测试只需要计算一个输入梯度的情况。
+- `test_check_grad_normal`中调用`check_grad`使用数值法检测梯度正确性和稳定性。
+  - 第一个参数`["X", "Y"]` : 指定对输入变量`X`、`Y`做梯度检测。
+  - 第二个参数`"Out"` : 指定前向网络最终的输出目标变量`Out`。
+  - 第三个参数`max_relative_error`：指定检测梯度时能容忍的最大错误值。
+- `test_check_grad_ingore_x`和`test_check_grad_ingore_y`分支用来测试只需要计算一个输入梯度的情况。
 
 
 ### 编译和执行单元测试

doc/howto/dev/new_op_en.md

@@ -293,41 +293,27 @@ class TestMulGradOp(GradientChecker):
             'Y': np.random.random((84, 100)).astype("float32")
         }
 
-    def test_cpu_gpu_compare(self):
-        self.compare_grad(self.op, self.inputs)
-
-    def test_normal(self):
+    def test_check_grad_normal(self):
         # mul op will enlarge the relative error
-        self.check_grad(
-            self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5)
+        self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5)
 
-    def test_ignore_x(self):
+    def test_check_grad_ingore_x(self):
         self.check_grad(
-            self.op,
-            self.inputs, ["Y"],
-            "Out",
-            max_relative_error=0.5,
-            no_grad_set={"X"})
+            ['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X"))
 
-    def test_ignore_y(self):
+    def test_check_grad_ingore_y(self):
         self.check_grad(
-            self.op,
-            self.inputs, ["X"],
-            "Out",
-            max_relative_error=0.5,
-            no_grad_set={"Y"})
+            ['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
 ```
 
 Some key points in the code above include:
 
 - `create_op("mul")` creates the backward operator's corresponding forward operator.
-- `compare_grad` compares results between utilizing the CPU and the GPU.
 - `test_normal` calls `check_grad` to validate scaling tests' correctness and stability through numeric methods.
-  - The first variable `self.op` denotes the forward operator.
-  - The second variable `self.inputs` denotes the input dictionary, which has its key value identical to its `ProtoMaker` definitions.
-  - The third variable `["X", "Y"]` appoints `X` and `Y` to be scale tested.
-  - The fourth variable `"Out"` points to the network's final output target `Out`.
-- `test_ignore_x` and `test_ignore_y`branches test the cases where there is only one scaling input.
+  - The first variable `["X", "Y"]` appoints `X` and `Y` to be scale tested.
+  - The second variable `"Out"` points to the network's final output target `Out`.
+  - The third variable `max_relative_error` points to the maximum relative tolerance error during scaling tests.
+- `test_check_grad_ingore_x` and `test_check_grad_ingore_y`branches test the cases where there is only one scaling input.
 
 ### Compiling and Running
 

paddle/framework/CMakeLists.txt

@@ -26,7 +26,7 @@ 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)
 cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry)
 
-cc_library(grad_op_builder SRCS grad_op_builder.cc DEPS operator)
+cc_library(grad_op_builder SRCS grad_op_builder.cc DEPS operator proto_desc)
 cc_library(op_registry SRCS op_registry.cc DEPS grad_op_builder op_proto_maker op_info)
 cc_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry)
 cc_test(grad_op_builder_test SRCS grad_op_builder_test.cc DEPS grad_op_builder op_registry add_op)

paddle/framework/data_type.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 <typeindex>
+#include "paddle/framework/framework.pb.h"
+
+namespace paddle {
+namespace framework {
+
+inline DataType ToDataType(std::type_index type) {
+  if (typeid(float).hash_code() == type.hash_code()) {
+    return DataType::FP32;
+  } else if (typeid(double).hash_code() == type.hash_code()) {
+    return DataType::FP64;
+  } else if (typeid(int).hash_code() == type.hash_code()) {
+    return DataType::INT32;
+  } else {
+    PADDLE_THROW("Not supported");
+    return static_cast<DataType>(-1);
+  }
+}
+
+}  // namespace framework
+}  // namespace paddle

paddle/framework/grad_op_builder.cc

@@ -54,5 +54,44 @@ OperatorBase* BuildGradOp(const OperatorBase* op) {
   return grad_info.Creator()(info.grad_op_type_, inputs, outputs, op->Attrs());
 }
 
+static void TransOpDescArg(const OpDescBind* src_op, const OpArgType& src_type,
+                           bool is_grad, OpDescBind* dst_op,
+                           const OpArgType& dst_type) {
+  PADDLE_ENFORCE(dst_op != nullptr,
+                 "Protobuf desc of gradient op must be initialized first.");
+  const auto& proto = OpInfoMap::Instance().Get(src_op->Type()).Proto();
+  const auto& src_arg_list =
+      src_type == OpArgType::IN ? proto.inputs() : proto.outputs();
+  for (const auto& arg : src_arg_list) {
+    if (arg.not_in_gradient() && !is_grad) continue;
+    const std::string src_name = arg.name();
+    std::vector<std::string> vars = src_type == OpArgType::IN
+                                        ? src_op->Input(src_name)
+                                        : src_op->Output(src_name);
+    if (is_grad) {
+      for (std::string& var : vars) {
+        var = GradVarName(var);
+      }
+    }
+    std::string dst_name = is_grad ? GradVarName(src_name) : src_name;
+    dst_type == OpArgType::IN ? dst_op->SetInput(dst_name, vars)
+                              : dst_op->SetOutput(dst_name, vars);
+  }
+}
+
+void CompleteGradOpDesc(const OpDescBind* forw_op, OpDescBind* grad_op) {
+  auto& info = OpInfoMap::Instance().Get(forw_op->Type());
+  PADDLE_ENFORCE(info.HasGradientOp());
+
+  grad_op->SetType(info.grad_op_type_);
+
+  TransOpDescArg(forw_op, OpArgType::IN, false, grad_op, OpArgType::IN);
+  TransOpDescArg(forw_op, OpArgType::OUT, false, grad_op, OpArgType::IN);
+  TransOpDescArg(forw_op, OpArgType::OUT, true, grad_op, OpArgType::IN);
+  TransOpDescArg(forw_op, OpArgType::IN, true, grad_op, OpArgType::OUT);
+
+  grad_op->SetAttrMap(forw_op->GetAttrMap());
+}
+
 }  // namespace framework
 }  // namespace paddle

paddle/framework/grad_op_builder.h

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #pragma once
 
+#include "paddle/framework/op_desc.h"
 #include "paddle/framework/operator.h"
 
 namespace paddle {
@@ -21,5 +22,7 @@ namespace framework {
 
 OperatorBase* BuildGradOp(const OperatorBase* op);
 
+void CompleteGradOpDesc(const OpDescBind* forw_op, OpDescBind* grad_op);
+
 }  // namespace framework
 }  // namespace paddle

paddle/framework/grad_op_builder_test.cc

@@ -120,3 +120,82 @@ TEST(GradOpBuilder, IOIgnoredInGradient) {
             std::vector<std::string>(
                 {f::GradVarName("in3_1"), f::GradVarName("in3_2")}));
 }
+
+TEST(GradOpDescBuilder, MutiInOut) {
+  f::OpDescBind *forw_op = new f::OpDescBind();
+  forw_op->SetType("mult_io");
+  forw_op->SetInput("In1", {"in1"});
+  forw_op->SetInput("In2_mult", {"in2_1", "in2_2", "in2_3"});
+  forw_op->SetInput("In3", {"in3"});
+  forw_op->SetOutput("Out1", {"out1"});
+  forw_op->SetOutput("Out2_mult", {"out2_1", "out2_2"});
+
+  f::OpDescBind *grad_op = new f::OpDescBind();
+  f::CompleteGradOpDesc(forw_op, grad_op);
+
+  EXPECT_EQ(grad_op->Type(), "mult_io_grad");
+  ASSERT_EQ(grad_op->InputNames().size(), 3UL + 2UL + 2UL);
+  EXPECT_EQ(grad_op->Input("In1"), std::vector<std::string>({"in1"}));
+  EXPECT_EQ(grad_op->Input("In2_mult"),
+            std::vector<std::string>({"in2_1", "in2_2", "in2_3"}));
+  EXPECT_EQ(grad_op->Input("In3"), std::vector<std::string>({"in3"}));
+  EXPECT_EQ(grad_op->Input("Out1"), std::vector<std::string>({"out1"}));
+  EXPECT_EQ(grad_op->Input("Out2_mult"),
+            std::vector<std::string>({"out2_1", "out2_2"}));
+  EXPECT_EQ(grad_op->Input(f::GradVarName("Out1")),
+            std::vector<std::string>({f::GradVarName("out1")}));
+  EXPECT_EQ(grad_op->Input(f::GradVarName("Out2_mult")),
+            std::vector<std::string>(
+                {f::GradVarName("out2_1"), f::GradVarName("out2_2")}));
+
+  ASSERT_EQ(grad_op->OutputNames().size(), 3UL);
+  EXPECT_EQ(grad_op->Output(f::GradVarName("In1")),
+            std::vector<std::string>({f::GradVarName("in1")}));
+  EXPECT_EQ(grad_op->Output(f::GradVarName("In2_mult")),
+            std::vector<std::string>({f::GradVarName("in2_1"),
+                                      f::GradVarName("in2_2"),
+                                      f::GradVarName("in2_3")}));
+  EXPECT_EQ(grad_op->Output(f::GradVarName("In3")),
+            std::vector<std::string>({f::GradVarName("in3")}));
+  delete forw_op;
+  delete grad_op;
+}
+
+TEST(GradOpDescBuilder, IOIgnoredInGradient) {
+  f::OpDescBind *forw_op = new f::OpDescBind();
+  forw_op->SetType("io_ignored");
+  forw_op->SetInput("In1", {"in1"});
+  forw_op->SetInput("In2_mult", {"in2_1", "in2_2"});
+  forw_op->SetInput("In3_mult", {"in3_1", "in3_2"});
+  forw_op->SetOutput("Out1_mult", {"out1_1", "out1_2"});
+  forw_op->SetOutput("Out2", {"out2"});
+
+  f::OpDescBind *grad_op = new f::OpDescBind();
+  f::CompleteGradOpDesc(forw_op, grad_op);
+
+  EXPECT_EQ(grad_op->Type(), "io_ignored_grad");
+  // 'In2' and 'Out2' are ignored in gradient calculating
+  ASSERT_EQ(grad_op->InputNames().size(), 2UL + 1UL + 2UL);
+  EXPECT_EQ(grad_op->Input("In1"), std::vector<std::string>({"in1"}));
+  EXPECT_EQ(grad_op->Input("In3_mult"),
+            std::vector<std::string>({"in3_1", "in3_2"}));
+  EXPECT_EQ(grad_op->Input("Out1_mult"),
+            std::vector<std::string>({"out1_1", "out1_2"}));
+  EXPECT_EQ(grad_op->Input(f::GradVarName("Out1_mult")),
+            std::vector<std::string>(
+                {f::GradVarName("out1_1"), f::GradVarName("out1_2")}));
+  EXPECT_EQ(grad_op->Input(f::GradVarName("Out2")),
+            std::vector<std::string>({f::GradVarName("out2")}));
+
+  ASSERT_EQ(grad_op->OutputNames().size(), 3UL);
+  EXPECT_EQ(grad_op->Output(f::GradVarName("In1")),
+            std::vector<std::string>({f::GradVarName("in1")}));
+  EXPECT_EQ(grad_op->Output(f::GradVarName("In2_mult")),
+            std::vector<std::string>(
+                {f::GradVarName("in2_1"), f::GradVarName("in2_2")}));
+  EXPECT_EQ(grad_op->Output(f::GradVarName("In3_mult")),
+            std::vector<std::string>(
+                {f::GradVarName("in3_1"), f::GradVarName("in3_2")}));
+  delete forw_op;
+  delete grad_op;
+}
\ No newline at end of file

paddle/framework/op_desc.cc

@@ -89,6 +89,12 @@ void OpDescBind::SetAttr(const std::string &name, const Attribute &v) {
   need_update_ = true;
 }
 
+void OpDescBind::SetAttrMap(
+    const std::unordered_map<std::string, Attribute> &attr_map) {
+  attrs_ = attr_map;
+  need_update_ = true;
+}
+
 Attribute OpDescBind::GetAttr(const std::string &name) const {
   auto it = attrs_.find(name);
   PADDLE_ENFORCE(it != attrs_.end(), "Attribute %s is not found", name);
@@ -101,6 +107,11 @@ int OpDescBind::GetBlockAttr(const std::string &name) const {
   return boost::get<BlockDesc *>(it->second)->idx();
 }
 
+const std::unordered_map<std::string, Attribute> &OpDescBind::GetAttrMap()
+    const {
+  return attrs_;
+}
+
 void OpDescBind::Sync() {
   if (need_update_) {
     this->op_desc_.mutable_inputs()->Clear();

paddle/framework/op_desc.h

@@ -60,10 +60,16 @@ class OpDescBind {
 
   void SetBlockAttr(const std::string &name, BlockDescBind &block);
 
+  // Only be used in C++
+  void SetAttrMap(const std::unordered_map<std::string, Attribute> &attr_map);
+
   Attribute GetAttr(const std::string &name) const;
 
   int GetBlockAttr(const std::string &name) const;
 
+  // Only be used in C++
+  const std::unordered_map<std::string, Attribute> &GetAttrMap() const;
+
  private:
   struct SetAttrDescVisitor : public boost::static_visitor<void> {
     explicit SetAttrDescVisitor(OpDesc::Attr *attr) : attr_(attr) {}

paddle/framework/op_registry.h

@@ -100,13 +100,39 @@ class OpRegistrar : public Registrar {
   }
 };
 
-template <typename PlaceType, typename KernelType>
+template <typename PlaceType, bool at_end, size_t I, typename... KernelType>
+struct OpKernelRegistrarFunctor;
+
+template <typename PlaceType, size_t I, typename... KernelTypes>
+struct OpKernelRegistrarFunctor<PlaceType, false, I, KernelTypes...> {
+  using KERNEL_TYPE =
+      typename std::tuple_element<I, std::tuple<KernelTypes...>>::type;
+
+  void operator()(const char* op_type) const {
+    using T = typename KERNEL_TYPE::ELEMENT_TYPE;
+    OperatorWithKernel::OpKernelKey key(ToDataType(std::type_index(typeid(T))),
+                                        PlaceType());
+    OperatorWithKernel::AllOpKernels()[op_type][key].reset(new KERNEL_TYPE);
+
+    constexpr auto size = std::tuple_size<std::tuple<KernelTypes...>>::value;
+    OpKernelRegistrarFunctor<PlaceType, I + 1 == size, I + 1, KernelTypes...>
+        func;
+    func(op_type);
+  }
+};
+
+template <typename PlaceType, size_t I, typename... KernelType>
+struct OpKernelRegistrarFunctor<PlaceType, true, I, KernelType...> {
+  void operator()(const char* op_type) const {}
+};
+
+// User can register many kernel in one place. The data type could be different.
+template <typename PlaceType, typename... KernelType>
 class OpKernelRegistrar : public Registrar {
  public:
   explicit OpKernelRegistrar(const char* op_type) {
-    OperatorWithKernel::OpKernelKey key;
-    key.place_ = PlaceType();
-    OperatorWithKernel::AllOpKernels()[op_type][key].reset(new KernelType);
+    OpKernelRegistrarFunctor<PlaceType, false, 0, KernelType...> func;
+    func(op_type);
   }
 };
 

paddle/framework/operator.cc

@@ -22,14 +22,14 @@ namespace framework {
 template <>
 Eigen::DefaultDevice& ExecutionContext::GetEigenDevice<
     platform::CPUPlace, Eigen::DefaultDevice>() const {
-  return *device_context_.get_eigen_device<Eigen::DefaultDevice>();
+  return *device_context_.GetEigenDevice<platform::CPUPlace>();
 }
 
 #ifndef PADDLE_ONLY_CPU
 template <>
 Eigen::GpuDevice&
 ExecutionContext::GetEigenDevice<platform::GPUPlace, Eigen::GpuDevice>() const {
-  return *device_context_.get_eigen_device<Eigen::GpuDevice>();
+  return *device_context_.GetEigenDevice<platform::GPUPlace>();
 }
 #endif
 

paddle/framework/operator.h

@@ -22,6 +22,7 @@ limitations under the License. */
 
 #include "op_info.h"
 #include "paddle/framework/attribute.h"
+#include "paddle/framework/data_type.h"
 #include "paddle/framework/framework.pb.h"
 #include "paddle/framework/lod_tensor.h"
 #include "paddle/framework/scope.h"
@@ -295,21 +296,6 @@ template <>
 std::vector<Tensor*> InferShapeContext::MultiOutput<Tensor>(
     const std::string& name) const;
 
-template <typename T>
-struct EigenDeviceConverter;
-
-template <>
-struct EigenDeviceConverter<platform::CPUPlace> {
-  using EigenDeviceType = Eigen::DefaultDevice;
-};
-
-#ifndef PADDLE_ONLY_CPU
-template <>
-struct EigenDeviceConverter<platform::GPUPlace> {
-  using EigenDeviceType = Eigen::GpuDevice;
-};
-#endif
-
 class ExecutionContext : public InferShapeContext {
  public:
   ExecutionContext(const OperatorBase& op, const Scope& scope,
@@ -317,8 +303,8 @@ class ExecutionContext : public InferShapeContext {
       : InferShapeContext(op, scope), device_context_(device_context) {}
 
   template <typename PlaceType,
-            typename DeviceType =
-                typename EigenDeviceConverter<PlaceType>::EigenDeviceType>
+            typename DeviceType = typename platform::EigenDeviceConverter<
+                PlaceType>::EigenDeviceType>
   DeviceType& GetEigenDevice() const;
 
   platform::Place GetPlace() const { return device_context_.GetPlace(); }
@@ -348,6 +334,32 @@ class RuntimeInferShapeContext : public InferShapeContextBase {
     return var != nullptr;
   }
 
+  bool HasInputs(const std::string& name) const {
+    auto inputs = op_.Inputs(name);
+    if (inputs.size() == 0UL) {
+      return false;
+    }
+    for (auto& input : inputs) {
+      if (scope_.FindVar(input) == nullptr) {
+        return false;
+      }
+    }
+    return true;
+  }
+
+  bool HasOutputs(const std::string& name) const {
+    auto outputs = op_.Outputs(name);
+    if (outputs.size() == 0UL) {
+      return false;
+    }
+    for (auto& output : outputs) {
+      if (scope_.FindVar(output) == nullptr) {
+        return false;
+      }
+    }
+    return true;
+  }
+
   DDim GetInputDim(const std::string& name) const {
     return GetDim(op_.Input(name));
   }
@@ -403,7 +415,7 @@ class RuntimeInferShapeContext : public InferShapeContextBase {
   const Scope& scope_;
 };
 
-class OpKernel {
+class OpKernelBase {
  public:
   /**
    * ExecutionContext is the only parameter of Kernel Run function.
@@ -414,33 +426,47 @@ class OpKernel {
 
   virtual void Compute(const ExecutionContext& context) const = 0;
 
-  virtual ~OpKernel() {}
+  virtual ~OpKernelBase() = default;
+};
+
+template <typename T>
+class OpKernel : public OpKernelBase {
+ public:
+  using ELEMENT_TYPE = T;
 };
 
 class OperatorWithKernel : public OperatorBase {
  public:
   struct OpKernelKey {
     platform::Place place_;
+    DataType data_type_;
 
-    OpKernelKey() = default;
-    explicit OpKernelKey(const platform::DeviceContext& dev_ctx) {
-      place_ = dev_ctx.GetPlace();
-    }
+    OpKernelKey(DataType data_type, platform::Place place)
+        : place_(place), data_type_(data_type) {}
+
+    OpKernelKey(DataType data_type, const platform::DeviceContext& dev_ctx)
+        : place_(dev_ctx.GetPlace()), data_type_(data_type) {}
 
     bool operator==(const OpKernelKey& o) const {
-      return platform::places_are_same_class(place_, o.place_);
+      return platform::places_are_same_class(place_, o.place_) &&
+             data_type_ == o.data_type_;
     }
   };
 
   struct OpKernelHash {
-    std::hash<bool> hash_;
+    std::hash<int> hash_;
     size_t operator()(const OpKernelKey& key) const {
-      return hash_(platform::is_gpu_place(key.place_));
+      int place = key.place_.which();
+      int data_type = static_cast<int>(key.data_type_);
+      int pre_hash = data_type << NUM_PLACE_TYPE_LIMIT_IN_BIT |
+                     (place & ((1 << NUM_PLACE_TYPE_LIMIT_IN_BIT) - 1));
+      return hash_(pre_hash);
     }
   };
 
   using OpKernelMap =
-      std::unordered_map<OpKernelKey, std::unique_ptr<OpKernel>, OpKernelHash>;
+      std::unordered_map<OpKernelKey, std::unique_ptr<OpKernelBase>,
+                         OpKernelHash>;
 
   OperatorWithKernel(const std::string& type, const VariableNameMap& inputs,
                      const VariableNameMap& outputs, const AttributeMap& attrs)
@@ -451,8 +477,10 @@ class OperatorWithKernel : public OperatorBase {
     RuntimeInferShapeContext infer_shape_ctx(*this, scope);
     this->InferShape(&infer_shape_ctx);
 
-    auto& opKernel = AllOpKernels().at(type_).at(OpKernelKey(dev_ctx));
-    opKernel->Compute(ExecutionContext(*this, scope, dev_ctx));
+    ExecutionContext ctx(*this, scope, dev_ctx);
+    auto& opKernel = AllOpKernels().at(type_).at(
+        OpKernelKey(IndicateDataType(ctx), dev_ctx));
+    opKernel->Compute(ctx);
   }
 
   static std::unordered_map<std::string /* op_type */, OpKernelMap>&
@@ -462,13 +490,43 @@ class OperatorWithKernel : public OperatorBase {
   }
 
   bool SupportGPU() const override {
-    OperatorWithKernel::OpKernelKey key;
-    key.place_ = platform::GPUPlace();
-    return OperatorWithKernel::AllOpKernels().at(type_).count(key) != 0;
+    auto& op_kernels = OperatorWithKernel::AllOpKernels().at(type_);
+    return std::any_of(op_kernels.begin(), op_kernels.end(),
+                       [](OpKernelMap::const_reference kern_pair) {
+                         return platform::is_gpu_place(kern_pair.first.place_);
+                       });
   }
 
  protected:
   virtual void InferShape(InferShapeContextBase* ctx) const = 0;
+
+  // indicate kernel DataType by input data. Defaultly all input data must be
+  // same.
+  virtual DataType IndicateDataType(const ExecutionContext& ctx) const {
+    auto& scope = ctx.scope();
+    int data_type = -1;
+    for (auto& input : this->inputs_) {
+      for (auto& ipt_name : input.second) {
+        auto* var = scope.FindVar(ipt_name);
+        if (var != nullptr) {
+          const Tensor* t = nullptr;
+          if (var->IsType<Tensor>()) {
+            t = &var->Get<Tensor>();
+          } else if (var->IsType<LoDTensor>()) {
+            t = &var->Get<LoDTensor>();
+          }
+          if (t != nullptr) {
+            int tmp = static_cast<int>(ToDataType(t->type()));
+            PADDLE_ENFORCE(tmp == data_type || data_type == -1,
+                           "DataType of Paddle Op must be same.");
+            data_type = tmp;
+          }
+        }
+      }
+    }
+    PADDLE_ENFORCE(data_type != -1, "DataType should be indicated by input");
+    return static_cast<DataType>(data_type);
+  }
 };
 
 }  // namespace framework

paddle/framework/operator_test.cc

@@ -114,10 +114,13 @@ class OpWithKernelTest : public OperatorWithKernel {
 
  protected:
   void InferShape(framework::InferShapeContextBase* ctx) const override {}
+  DataType IndicateDataType(const ExecutionContext& ctx) const override {
+    return DataType::FP32;
+  }
 };
 
 template <typename T1, typename T2>
-class CPUKernelTest : public OpKernel {
+class CPUKernelTest : public OpKernel<float> {
  public:
   void Compute(const ExecutionContext& ctx) const {
     std::cout << "this is cpu kernel" << std::endl;
@@ -144,7 +147,7 @@ class OpKernelTestMultiInputsProtoAndCheckerMaker
   }
 };
 
-class CPUKernalMultiInputsTest : public OpKernel {
+class CPUKernalMultiInputsTest : public OpKernel<float> {
  public:
   void Compute(const ExecutionContext& ctx) const {
     auto xs = ctx.op().Inputs("xs");

paddle/framework/shape_inference.h

@@ -24,6 +24,10 @@ class InferShapeContextBase {
   virtual ~InferShapeContextBase() {}
   virtual bool HasInput(const std::string &name) const = 0;
   virtual bool HasOutput(const std::string &name) const = 0;
+
+  virtual bool HasInputs(const std::string &name) const = 0;
+  virtual bool HasOutputs(const std::string &name) const = 0;
+
   virtual framework::DDim GetInputDim(const std::string &name) const = 0;
   std::vector<framework::DDim> GetInputsDim(const std::string &name) const {
     const std::vector<std::string> &names = Inputs(name);

paddle/framework/tensor.h

@@ -29,20 +29,10 @@ limitations under the License. */
 
 namespace paddle {
 
-namespace pybind {
-namespace details {
-template <bool less, size_t i, typename... args>
-struct CastToPyBufferImpl;
-}
-}  // namespace pybind
-
 namespace framework {
 
 class Tensor {
  public:
-  template <bool less, size_t i, typename... args>
-  friend struct pybind::details::CastToPyBufferImpl;
-
   template <typename T, size_t D, int MajorType, typename IndexType>
   friend struct EigenTensor;
 
@@ -119,6 +109,8 @@ class Tensor {
     return holder_->place();
   }
 
+  std::type_index type() const { return holder_->type(); }
+
  private:
   template <typename T>
   inline void check_memory_size() const;

paddle/gserver/tests/test_MKLDNN.cpp

@@ -215,13 +215,13 @@ struct testActDesc {
 static void getAddtoConfig(TestConfig& cfg, const testActDesc& pm) {
   cfg.biasSize = 0;
   cfg.layerConfig.set_type("addto");
-  size_t layerSize = pm.ih * pm.ih * pm.iw;
+  size_t layerSize = pm.ic * pm.ih * pm.iw;
   cfg.layerConfig.set_size(layerSize);
   cfg.inputDefs.push_back({INPUT_DATA, "layer_0", layerSize, 0});
   cfg.layerConfig.add_inputs();
 }
 
-void testActivation(std::string& actType, const testActDesc& pm) {
+void testActivation(std::string actType, const testActDesc& pm) {
   // TODO(TJ): remove me when paddle support elu activation
   if (actType == "mkldnn_elu") {
     return;
@@ -240,6 +240,7 @@ TEST(MKLDNNActivation, Activations) {
   for (auto type : types) {
     /* bs, c, h, w*/
     testActivation(type, {16, 64, 32, 32});
+    testActivation(type, {2, 8, 1, 1});
   }
 }
 

paddle/math/RowBuffer.h

@@ -99,7 +99,11 @@ public:
   /**
    * @brief clear local buffer. It only affect auto-growth buffer.
    */
-  inline void clear() { rowStore_.clear(); }
+  inline void clear() {
+    // swap an empty vector to it to free the memory.
+    std::vector<real, AlignedAllocator<real, 32>> empty;
+    rowStore_.swap(empty);
+  }
 
   /**
    * @brief get current number of rows.

paddle/operators/CMakeLists.txt

@@ -55,12 +55,25 @@ function(op_library TARGET)
         set(pybind_flag 1)
     endif()
 
+    if ("${TARGET}" STREQUAL "pool_op")
+        set(pybind_flag 1)
+        # It's enough to just adding one operator to pybind
+        file(APPEND ${pybind_file} "USE_OP(pool2d);\n")
+    endif()
+
     # activation_op contains several operators
     if ("${TARGET}" STREQUAL "activation_op")
         set(pybind_flag 1)
         # It's enough to just adding one operator to pybind
         file(APPEND ${pybind_file} "USE_OP(sigmoid);\n")
     endif()
+    
+    # reduce_op contains several operators
+    if ("${TARGET}" STREQUAL "reduce_op")
+        set(pybind_flag 1)
+        # It's enough to just adding one operator to pybind
+        file(APPEND ${pybind_file} "USE_OP(reduce_sum);\n")
+    endif()
 
     if ("${TARGET}" STREQUAL "pool_with_index_op")
         set(pybind_flag 1)
@@ -100,8 +113,8 @@ set(DEPS_OPS
 op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
   DEPS framework_proto tensor net_op)
 op_library(cond_op SRCS cond_op.cc DEPS framework_proto tensor operator net_op)
-op_library(cross_entropy_op DEPS cross_entropy_function)
-op_library(softmax_with_cross_entropy_op DEPS cross_entropy_function softmax_function)
+op_library(cross_entropy_op DEPS cross_entropy)
+op_library(softmax_with_cross_entropy_op DEPS cross_entropy softmax)
 
 list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
 foreach(src ${GENERAL_OPS})

paddle/operators/accuracy_op.cu

@@ -47,7 +47,7 @@ __global__ void AccuracyCudaKernel(const int N, const int D, const int* Xdata,
 }
 
 template <typename T>
-class AccuracyOpCUDAKernel : public framework::OpKernel {
+class AccuracyOpCUDAKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),

paddle/operators/accuracy_op.h

@@ -35,7 +35,7 @@ template <typename T, int MajorType = Eigen::RowMajor,
 using EigenScalar = framework::EigenScalar<T, MajorType, IndexType>;
 
 template <typename Place, typename T>
-class AccuracyKernel : public framework::OpKernel {
+class AccuracyKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto* inference = ctx.Input<Tensor>("Inference");

paddle/operators/activation_op.cc

@@ -132,6 +132,17 @@ class SquareOpMaker : public framework::OpProtoAndCheckerMaker {
   }
 };
 
+class SoftsignOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  SoftsignOpMaker(framework::OpProto *proto,
+                  framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "Input of Softsign operator");
+    AddOutput("Y", "Output of Softsign operator");
+    AddComment("Softsign activation operator, softsign(x) = x / (1 + |x|)");
+  }
+};
+
 template <typename AttrType>
 class BReluOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
@@ -195,111 +206,57 @@ class STanhOpMaker : public framework::OpProtoAndCheckerMaker {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
+
 REGISTER_OP(sigmoid, ops::ActivationOp, ops::SigmoidOpMaker, sigmoid_grad,
             ops::ActivationOpGrad);
-REGISTER_OP_CPU_KERNEL(sigmoid,
-                       ops::ActivationKernel<paddle::platform::CPUPlace, float,
-                                             ops::SigmoidFunctor<float>>);
-REGISTER_OP_CPU_KERNEL(
-    sigmoid_grad, ops::ActivationGradKernel<paddle::platform::CPUPlace, float,
-                                            ops::SigmoidGradFunctor<float>>);
 
 REGISTER_OP(exp, ops::ActivationOp, ops::ExpOpMaker, exp_grad,
             ops::ActivationOpGrad);
-REGISTER_OP_CPU_KERNEL(
-    exp,
-    ops::ActivationKernel<paddle::platform::CPUPlace, float, ops::ExpFunctor>);
-REGISTER_OP_CPU_KERNEL(exp_grad,
-                       ops::ActivationGradKernel<paddle::platform::CPUPlace,
-                                                 float, ops::ExpGradFunctor>);
 
 REGISTER_OP(relu, ops::ActivationOp, ops::ReluOpMaker, relu_grad,
             ops::ActivationOpGrad);
-REGISTER_OP_CPU_KERNEL(relu,
-                       ops::ActivationKernel<paddle::platform::CPUPlace, float,
-                                             ops::ReluFunctor<float>>);
-REGISTER_OP_CPU_KERNEL(
-    relu_grad, ops::ActivationGradKernel<paddle::platform::CPUPlace, float,
-                                         ops::ReluGradFunctor<float>>);
 
 REGISTER_OP(tanh, ops::ActivationOp, ops::TanhOpMaker, tanh_grad,
             ops::ActivationOpGrad);
-REGISTER_OP_CPU_KERNEL(
-    tanh,
-    ops::ActivationKernel<paddle::platform::CPUPlace, float, ops::TanhFunctor>);
-REGISTER_OP_CPU_KERNEL(
-    tanh_grad, ops::ActivationGradKernel<paddle::platform::CPUPlace, float,
-                                         ops::TanhGradFunctor<float>>);
 
 REGISTER_OP(sqrt, ops::ActivationOp, ops::SqrtOpMaker, sqrt_grad,
             ops::ActivationOpGrad);
-REGISTER_OP_CPU_KERNEL(
-    sqrt,
-    ops::ActivationKernel<paddle::platform::CPUPlace, float, ops::SqrtFunctor>);
-REGISTER_OP_CPU_KERNEL(
-    sqrt_grad, ops::ActivationGradKernel<paddle::platform::CPUPlace, float,
-                                         ops::SqrtGradFunctor<float>>);
 
 REGISTER_OP(abs, ops::ActivationOp, ops::AbsOpMaker, abs_grad,
             ops::ActivationOpGrad);
-REGISTER_OP_CPU_KERNEL(
-    abs,
-    ops::ActivationKernel<paddle::platform::CPUPlace, float, ops::AbsFunctor>);
-REGISTER_OP_CPU_KERNEL(abs_grad,
-                       ops::ActivationGradKernel<paddle::platform::CPUPlace,
-                                                 float, ops::AbsGradFunctor>);
 
 REGISTER_OP(reciprocal, ops::ActivationOp, ops::ReciprocalOpMaker,
             reciprocal_grad, ops::ActivationOpGrad);
-REGISTER_OP_CPU_KERNEL(reciprocal,
-                       ops::ActivationKernel<paddle::platform::CPUPlace, float,
-                                             ops::ReciprocalFunctor<float>>);
-REGISTER_OP_CPU_KERNEL(
-    reciprocal_grad,
-    ops::ActivationGradKernel<paddle::platform::CPUPlace, float,
-                              ops::ReciprocalGradFunctor<float>>);
 
 REGISTER_OP(log, ops::ActivationOp, ops::LogOpMaker, log_grad,
             ops::ActivationOpGrad);
-REGISTER_OP_CPU_KERNEL(
-    log,
-    ops::ActivationKernel<paddle::platform::CPUPlace, float, ops::LogFunctor>);
-REGISTER_OP_CPU_KERNEL(
-    log_grad, ops::ActivationGradKernel<paddle::platform::CPUPlace, float,
-                                        ops::LogGradFunctor<float>>);
 
 REGISTER_OP(square, ops::ActivationOp, ops::SquareOpMaker, square_grad,
             ops::ActivationOpGrad);
-REGISTER_OP_CPU_KERNEL(square,
-                       ops::ActivationKernel<paddle::platform::CPUPlace, float,
-                                             ops::SquareFunctor>);
-REGISTER_OP_CPU_KERNEL(
-    square_grad, ops::ActivationGradKernel<paddle::platform::CPUPlace, float,
-                                           ops::SquareGradFunctor<float>>);
+
+REGISTER_OP(softsign, ops::ActivationOp, ops::SoftsignOpMaker, softsign_grad,
+            ops::ActivationOpGrad);
 
 REGISTER_OP(brelu, ops::ActivationOp, ops::BReluOpMaker<float>, brelu_grad,
             ops::ActivationOpGrad);
-REGISTER_OP_CPU_KERNEL(brelu,
-                       ops::BReluKernel<paddle::platform::CPUPlace, float>);
-REGISTER_OP_CPU_KERNEL(brelu_grad,
-                       ops::BReluGradKernel<paddle::platform::CPUPlace, float>);
 
 REGISTER_OP(soft_relu, ops::ActivationOp, ops::SoftReluOpMaker<float>,
             soft_relu_grad, ops::ActivationOpGrad);
-REGISTER_OP_CPU_KERNEL(soft_relu,
-                       ops::SoftReluKernel<paddle::platform::CPUPlace, float>);
-REGISTER_OP_CPU_KERNEL(
-    soft_relu_grad, ops::SoftReluGradKernel<paddle::platform::CPUPlace, float>);
 
 REGISTER_OP(pow, ops::ActivationOp, ops::PowOpMaker<float>, pow_grad,
             ops::ActivationOpGrad);
-REGISTER_OP_CPU_KERNEL(pow, ops::PowKernel<paddle::platform::CPUPlace, float>);
-REGISTER_OP_CPU_KERNEL(pow_grad,
-                       ops::PowGradKernel<paddle::platform::CPUPlace, float>);
 
 REGISTER_OP(stanh, ops::ActivationOp, ops::STanhOpMaker<float>, stanh_grad,
             ops::ActivationOpGrad);
-REGISTER_OP_CPU_KERNEL(stanh,
-                       ops::STanhKernel<paddle::platform::CPUPlace, float>);
-REGISTER_OP_CPU_KERNEL(stanh_grad,
-                       ops::STanhGradKernel<paddle::platform::CPUPlace, float>);
+
+#define REGISTER_ACTIVATION_CPU_KERNEL(act_type, functor, grad_functor)        \
+  REGISTER_OP_CPU_KERNEL(                                                      \
+      act_type,                                                                \
+      paddle::operators::ActivationKernel<paddle::platform::CPUPlace,          \
+                                          paddle::operators::functor<float>>); \
+  REGISTER_OP_CPU_KERNEL(act_type##_grad,                                      \
+                         paddle::operators::ActivationGradKernel<              \
+                             paddle::platform::CPUPlace,                       \
+                             paddle::operators::grad_functor<float>>);
+
+FOR_EACH_KERNEL_FUNCTOR(REGISTER_ACTIVATION_CPU_KERNEL);

paddle/operators/activation_op.cu

@@ -15,86 +15,14 @@
 #define EIGEN_USE_GPU
 #include "paddle/operators/activation_op.h"
 
-namespace ops = paddle::operators;
-
-REGISTER_OP_GPU_KERNEL(sigmoid,
-                       ops::ActivationKernel<paddle::platform::GPUPlace, float,
-                                             ops::SigmoidFunctor<float>>);
-REGISTER_OP_GPU_KERNEL(
-    sigmoid_grad, ops::ActivationGradKernel<paddle::platform::GPUPlace, float,
-                                            ops::SigmoidGradFunctor<float>>);
-
-REGISTER_OP_GPU_KERNEL(
-    exp,
-    ops::ActivationKernel<paddle::platform::GPUPlace, float, ops::ExpFunctor>);
-REGISTER_OP_GPU_KERNEL(exp_grad,
-                       ops::ActivationGradKernel<paddle::platform::GPUPlace,
-                                                 float, ops::ExpGradFunctor>);
-REGISTER_OP_GPU_KERNEL(relu,
-                       ops::ActivationKernel<paddle::platform::GPUPlace, float,
-                                             ops::ReluFunctor<float>>);
-REGISTER_OP_GPU_KERNEL(
-    relu_grad, ops::ActivationGradKernel<paddle::platform::GPUPlace, float,
-                                         ops::ReluGradFunctor<float>>);
-
-REGISTER_OP_GPU_KERNEL(
-    tanh,
-    ops::ActivationKernel<paddle::platform::GPUPlace, float, ops::TanhFunctor>);
-REGISTER_OP_GPU_KERNEL(
-    tanh_grad, ops::ActivationGradKernel<paddle::platform::GPUPlace, float,
-                                         ops::TanhGradFunctor<float>>);
-
-REGISTER_OP_GPU_KERNEL(
-    sqrt,
-    ops::ActivationKernel<paddle::platform::GPUPlace, float, ops::SqrtFunctor>);
-REGISTER_OP_GPU_KERNEL(
-    sqrt_grad, ops::ActivationGradKernel<paddle::platform::GPUPlace, float,
-                                         ops::SqrtGradFunctor<float>>);
-
-REGISTER_OP_GPU_KERNEL(
-    abs,
-    ops::ActivationKernel<paddle::platform::GPUPlace, float, ops::AbsFunctor>);
-REGISTER_OP_GPU_KERNEL(abs_grad,
-                       ops::ActivationGradKernel<paddle::platform::GPUPlace,
-                                                 float, ops::AbsGradFunctor>);
-
-REGISTER_OP_GPU_KERNEL(reciprocal,
-                       ops::ActivationKernel<paddle::platform::GPUPlace, float,
-                                             ops::ReciprocalFunctor<float>>);
-REGISTER_OP_GPU_KERNEL(
-    reciprocal_grad,
-    ops::ActivationGradKernel<paddle::platform::GPUPlace, float,
-                              ops::ReciprocalGradFunctor<float>>);
-
-REGISTER_OP_GPU_KERNEL(
-    log,
-    ops::ActivationKernel<paddle::platform::GPUPlace, float, ops::LogFunctor>);
-REGISTER_OP_GPU_KERNEL(
-    log_grad, ops::ActivationGradKernel<paddle::platform::GPUPlace, float,
-                                        ops::LogGradFunctor<float>>);
-
-REGISTER_OP_GPU_KERNEL(square,
-                       ops::ActivationKernel<paddle::platform::GPUPlace, float,
-                                             ops::SquareFunctor>);
-REGISTER_OP_GPU_KERNEL(
-    square_grad, ops::ActivationGradKernel<paddle::platform::GPUPlace, float,
-                                           ops::SquareGradFunctor<float>>);
-
-REGISTER_OP_GPU_KERNEL(brelu,
-                       ops::BReluKernel<paddle::platform::GPUPlace, float>);
-REGISTER_OP_GPU_KERNEL(brelu_grad,
-                       ops::BReluGradKernel<paddle::platform::GPUPlace, float>);
-
-REGISTER_OP_GPU_KERNEL(soft_relu,
-                       ops::SoftReluKernel<paddle::platform::GPUPlace, float>);
-REGISTER_OP_GPU_KERNEL(
-    soft_relu_grad, ops::SoftReluGradKernel<paddle::platform::GPUPlace, float>);
-
-REGISTER_OP_GPU_KERNEL(pow, ops::PowKernel<paddle::platform::GPUPlace, float>);
-REGISTER_OP_GPU_KERNEL(pow_grad,
-                       ops::PowGradKernel<paddle::platform::GPUPlace, float>);
-
-REGISTER_OP_GPU_KERNEL(stanh,
-                       ops::STanhKernel<paddle::platform::GPUPlace, float>);
-REGISTER_OP_GPU_KERNEL(stanh_grad,
-                       ops::STanhGradKernel<paddle::platform::GPUPlace, float>);
+#define REGISTER_ACTIVATION_GPU_KERNEL(act_type, functor, grad_functor)        \
+  REGISTER_OP_GPU_KERNEL(                                                      \
+      act_type,                                                                \
+      paddle::operators::ActivationKernel<paddle::platform::GPUPlace,          \
+                                          paddle::operators::functor<float>>); \
+  REGISTER_OP_GPU_KERNEL(act_type##_grad,                                      \
+                         paddle::operators::ActivationGradKernel<              \
+                             paddle::platform::GPUPlace,                       \
+                             paddle::operators::grad_functor<float>>);
+
+FOR_EACH_KERNEL_FUNCTOR(REGISTER_ACTIVATION_GPU_KERNEL);

paddle/operators/activation_op.h

@@ -19,9 +19,12 @@
 namespace paddle {
 namespace operators {
 
-template <typename Place, typename T, typename Functor>
-class ActivationKernel : public framework::OpKernel {
+template <typename Place, typename Functor>
+class ActivationKernel
+    : public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
  public:
+  using T = typename Functor::ELEMENT_TYPE;
+
   void Compute(const framework::ExecutionContext& context) const override {
     auto* X = context.Input<framework::Tensor>("X");
     auto* Y = context.Output<framework::Tensor>("Y");
@@ -31,13 +34,20 @@ class ActivationKernel : public framework::OpKernel {
     auto y = framework::EigenVector<T>::Flatten(*Y);
     auto place = context.GetEigenDevice<Place>();
     Functor functor;
+
+    auto attrs = functor.GetAttrs();
+    for (auto& attr : attrs) {
+      *attr.second = context.Attr<float>(attr.first);
+    }
     functor(place, x, y);
   }
 };
 
-template <typename Place, typename T, typename Functor>
-class ActivationGradKernel : public framework::OpKernel {
+template <typename Place, typename Functor>
+class ActivationGradKernel
+    : public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
  public:
+  using T = typename Functor::ELEMENT_TYPE;
   void Compute(const framework::ExecutionContext& context) const override {
     auto* X = context.Input<framework::Tensor>("X");
     auto* Y = context.Input<framework::Tensor>("Y");
@@ -51,303 +61,322 @@ class ActivationGradKernel : public framework::OpKernel {
     auto dx = framework::EigenVector<T>::Flatten(*dX);
     auto place = context.GetEigenDevice<Place>();
     Functor functor;
+    auto attrs = functor.GetAttrs();
+    for (auto& attr : attrs) {
+      *attr.second = context.Attr<float>(attr.first);
+    }
     functor(place, x, y, dy, dx);
   }
 };
 
+template <typename T>
+struct BaseActivationFunctor {
+  using ELEMENT_TYPE = T;
+
+  using AttrPair = std::vector<std::pair<const char*, float*>>;
+
+  AttrPair GetAttrs() { return AttrPair(); }
+};
+
 // sigmoid(x) = 1 / (1 + exp(-x))
 template <typename T>
-struct SigmoidFunctor {
+struct SigmoidFunctor : public BaseActivationFunctor<T> {
   template <typename Device, typename X, typename Y>
-  void operator()(Device d, X x, Y y) {
+  void operator()(Device d, X x, Y y) const {
     y.device(d) = static_cast<T>(1) / (static_cast<T>(1) + (-x).exp());
   }
 };
 
 template <typename T>
-struct SigmoidGradFunctor {
+struct SigmoidGradFunctor : public BaseActivationFunctor<T> {
   template <typename Device, typename X, typename Y, typename dY, typename dX>
-  void operator()(Device d, X x, Y y, dY dy, dX dx) {
+  void operator()(Device d, X x, Y y, dY dy, dX dx) const {
     dx.device(d) = dy * y * (static_cast<T>(1) - y);
   }
 };
 
 // exp(x) = e^x
-struct ExpFunctor {
+template <typename T>
+struct ExpFunctor : public BaseActivationFunctor<T> {
   template <typename Device, typename X, typename Y>
-  void operator()(Device d, X x, Y y) {
+  void operator()(Device d, X x, Y y) const {
     y.device(d) = x.exp();
   }
 };
 
-struct ExpGradFunctor {
+template <typename T>
+struct ExpGradFunctor : public BaseActivationFunctor<T> {
   template <typename Device, typename X, typename Y, typename dY, typename dX>
-  void operator()(Device d, X x, Y y, dY dy, dX dx) {
+  void operator()(Device d, X x, Y y, dY dy, dX dx) const {
     dx.device(d) = dy * y;
   }
 };
 
 // relu(x) = max(x, 0)
 template <typename T>
-struct ReluFunctor {
+struct ReluFunctor : public BaseActivationFunctor<T> {
   template <typename Device, typename X, typename Y>
-  void operator()(Device d, X x, Y y) {
+  void operator()(Device d, X x, Y y) const {
     y.device(d) = x.cwiseMax(static_cast<T>(0));
   }
 };
 
 template <typename T>
-struct ReluGradFunctor {
+struct ReluGradFunctor : public BaseActivationFunctor<T> {
   template <typename Device, typename X, typename Y, typename dY, typename dX>
-  void operator()(Device d, X x, Y y, dY dy, dX dx) {
+  void operator()(Device d, X x, Y y, dY dy, dX dx) const {
     dx.device(d) = dy * (x > static_cast<T>(0)).template cast<T>();
   }
 };
 
 // tanh(x) = (exp(x) - exp(-x)) / (exp(x) + exp(-x))
-struct TanhFunctor {
+template <typename T>
+struct TanhFunctor : public BaseActivationFunctor<T> {
   template <typename Device, typename X, typename Y>
-  void operator()(Device d, X x, Y y) {
+  void operator()(Device d, X x, Y y) const {
     y.device(d) = x.tanh();
   }
 };
 
 template <typename T>
-struct TanhGradFunctor {
+struct TanhGradFunctor : public BaseActivationFunctor<T> {
   template <typename Device, typename X, typename Y, typename dY, typename dX>
-  void operator()(Device d, X x, Y y, dY dy, dX dx) {
+  void operator()(Device d, X x, Y y, dY dy, dX dx) const {
     dx.device(d) = dy * (static_cast<T>(1) - y * y);
   }
 };
 
 // sqrt(x) = x^(1/2)
-struct SqrtFunctor {
+template <typename T>
+struct SqrtFunctor : public BaseActivationFunctor<T> {
   template <typename Device, typename X, typename Y>
-  void operator()(Device d, X x, Y y) {
+  void operator()(Device d, X x, Y y) const {
     y.device(d) = x.sqrt();
   }
 };
 
 template <typename T>
-struct SqrtGradFunctor {
+struct SqrtGradFunctor : public BaseActivationFunctor<T> {
   template <typename Device, typename X, typename Y, typename dY, typename dX>
-  void operator()(Device d, X x, Y y, dY dy, dX dx) {
+  void operator()(Device d, X x, Y y, dY dy, dX dx) const {
     const Y y_conj = Eigen::numext::conj(y);
     dx.device(d) = static_cast<T>(0.5) * dy / y_conj;
   }
 };
 
 // abs(x) = |x|
-struct AbsFunctor {
+template <typename T>
+struct AbsFunctor : public BaseActivationFunctor<T> {
   template <typename Device, typename X, typename Y>
-  void operator()(Device d, X x, Y y) {
+  void operator()(Device d, X x, Y y) const {
     y.device(d) = x.abs();
   }
 };
 
-struct AbsGradFunctor {
+template <typename T>
+struct AbsGradFunctor : public BaseActivationFunctor<T> {
   template <typename Device, typename X, typename Y, typename dY, typename dX>
-  void operator()(Device d, X x, Y y, dY dy, dX dx) {
+  void operator()(Device d, X x, Y y, dY dy, dX dx) const {
     dx.device(d) = dy * x.sign();
   }
 };
 
 // reciprocal(x) = 1 / x
 template <typename T>
-struct ReciprocalFunctor {
+struct ReciprocalFunctor : public BaseActivationFunctor<T> {
   template <typename Device, typename X, typename Y>
-  void operator()(Device d, X x, Y y) {
+  void operator()(Device d, X x, Y y) const {
     y.device(d) = static_cast<T>(1) / x;
   }
 };
 
 template <typename T>
-struct ReciprocalGradFunctor {
+struct ReciprocalGradFunctor : public BaseActivationFunctor<T> {
   template <typename Device, typename X, typename Y, typename dY, typename dX>
-  void operator()(Device d, X x, Y y, dY dy, dX dx) {
+  void operator()(Device d, X x, Y y, dY dy, dX dx) const {
     dx.device(d) = dy * static_cast<T>(-1) * y * y;
   }
 };
 
 // log(x) = natural logarithm of x
-struct LogFunctor {
+template <typename T>
+struct LogFunctor : public BaseActivationFunctor<T> {
   template <typename Device, typename X, typename Y>
-  void operator()(Device d, X x, Y y) {
+  void operator()(Device d, X x, Y y) const {
     y.device(d) = x.log();
   }
 };
 
 template <typename T>
-struct LogGradFunctor {
+struct LogGradFunctor : public BaseActivationFunctor<T> {
   template <typename Device, typename X, typename Y, typename dY, typename dX>
-  void operator()(Device d, X x, Y y, dY dy, dX dx) {
+  void operator()(Device d, X x, Y y, dY dy, dX dx) const {
     dx.device(d) = dy * (static_cast<T>(1) / x);
   }
 };
 
 // square(x) = x^2
-struct SquareFunctor {
+template <typename T>
+struct SquareFunctor : public BaseActivationFunctor<T> {
   template <typename Device, typename X, typename Y>
-  void operator()(Device d, X x, Y y) {
+  void operator()(Device d, X x, Y y) const {
     y.device(d) = x.square();
   }
 };
 
 template <typename T>
-struct SquareGradFunctor {
+struct SquareGradFunctor : public BaseActivationFunctor<T> {
   template <typename Device, typename X, typename Y, typename dY, typename dX>
-  void operator()(Device d, X x, Y y, dY dy, dX dx) {
+  void operator()(Device d, X x, Y y, dY dy, dX dx) const {
     dx.device(d) = dy * static_cast<T>(2) * x;
   }
 };
 
-template <typename Place, typename T, typename AttrType = T>
-class BReluKernel : public framework::OpKernel {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    auto* X = context.Input<framework::Tensor>("X");
-    auto* Y = context.Output<framework::Tensor>("Y");
-    auto t_min = static_cast<T>(context.Attr<AttrType>("t_min"));
-    auto t_max = static_cast<T>(context.Attr<AttrType>("t_max"));
-    Y->mutable_data<T>(context.GetPlace());
+template <typename T>
+struct BReluFunctor : public BaseActivationFunctor<T> {
+  float t_min;
+  float t_max;
+
+  // NOTE: Explicit hides the `BaseActivationFunctor<T>::GetAttrs`
+  // not polymorphism for speed.
+  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
+    return {{"t_min", &t_min}, {"t_max", &t_max}};
+  }
 
-    auto x = framework::EigenVector<T>::Flatten(*X);
-    auto y = framework::EigenVector<T>::Flatten(*Y);
-    auto place = context.GetEigenDevice<Place>();
-    y.device(place) = x.cwiseMax(t_min).cwiseMin(t_max);
+  template <typename Device, typename X, typename Y>
+  void operator()(Device d, X x, Y y) const {
+    y.device(d) = x.cwiseMax(t_min).cwiseMin(t_max);
   }
 };
 
-template <typename Place, typename T, typename AttrType = T>
-class BReluGradKernel : public framework::OpKernel {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    auto* X = context.Input<framework::Tensor>("X");
-    auto* dY = context.Input<framework::Tensor>(framework::GradVarName("Y"));
-    auto* dX = context.Output<framework::Tensor>(framework::GradVarName("X"));
-    auto t_min = static_cast<T>(context.Attr<AttrType>("t_min"));
-    auto t_max = static_cast<T>(context.Attr<AttrType>("t_max"));
-    dX->mutable_data<T>(context.GetPlace());
+template <typename T>
+struct BReluGradFunctor : public BaseActivationFunctor<T> {
+  float t_min;
+  float t_max;
+  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
+    return {{"t_min", &t_min}, {"t_max", &t_max}};
+  }
+  template <typename Device, typename X, typename Y, typename dY, typename dX>
+  void operator()(Device d, X x, Y y, dY dy, dX dx) const {
+    dx.device(d) = dy * ((x > t_min) * (x < t_max)).template cast<T>();
+  }
+};
 
-    auto dy = framework::EigenVector<T>::Flatten(*dY);
-    auto x = framework::EigenVector<T>::Flatten(*X);
-    auto dx = framework::EigenVector<T>::Flatten(*dX);
-    auto place = context.GetEigenDevice<Place>();
+// softsign(x) = x / (1 + |x|)
+template <typename T>
+struct SoftsignFunctor : public BaseActivationFunctor<T> {
+  template <typename Device, typename X, typename Y>
+  void operator()(Device d, X x, Y y) {
+    y.device(d) = x / (static_cast<T>(1) + x.abs());
+  }
+};
 
-    dx.device(place) = dy * ((x > t_min) * (x < t_max)).template cast<T>();
+// d(softsign(x))/dx = 1 / (1 + |x|)^2
+// Taken from https://en.wikipedia.org/wiki/Activation_function
+template <typename T>
+struct SoftsignGradFunctor : public BaseActivationFunctor<T> {
+  template <typename Device, typename X, typename Y, typename dY, typename dX>
+  void operator()(Device d, X x, Y y, dY dy, dX dx) {
+    dx.device(d) =
+        dy * (static_cast<T>(1) / (static_cast<T>(1) + x.abs()).square());
   }
 };
 
-template <typename Place, typename T, typename AttrType = T>
-class SoftReluKernel : public framework::OpKernel {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    auto* X = context.Input<framework::Tensor>("X");
-    auto* Y = context.Output<framework::Tensor>("Y");
-    auto threshold = static_cast<T>(context.Attr<AttrType>("threshold"));
-    Y->mutable_data<T>(context.GetPlace());
+template <typename T>
+struct SoftReluFunctor : public BaseActivationFunctor<T> {
+  float threshold;
+  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
+    return {{"threshold", &threshold}};
+  }
 
-    auto x = framework::EigenVector<T>::Flatten(*X);
-    auto y = framework::EigenVector<T>::Flatten(*Y);
-    auto place = context.GetEigenDevice<Place>();
-    auto temp = x.cwiseMax(-threshold).cwiseMin(threshold).eval();
-    y.device(place) = (static_cast<T>(1) + temp.exp()).log();
+  template <typename Device, typename X, typename Y>
+  void operator()(Device d, X x, Y y) const {
+    auto temp = x.cwiseMax(-threshold).cwiseMin(threshold);
+    y.device(d) = (static_cast<T>(1) + temp.exp()).log();
   }
 };
 
-template <typename Place, typename T, typename AttrType = T>
-class SoftReluGradKernel : public framework::OpKernel {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    auto* X = context.Input<framework::Tensor>("X");
-    auto* Y = context.Input<framework::Tensor>("Y");
-    auto* dY = context.Input<framework::Tensor>(framework::GradVarName("Y"));
-    auto* dX = context.Output<framework::Tensor>(framework::GradVarName("X"));
-    auto threshold = static_cast<T>(context.Attr<AttrType>("threshold"));
-    dX->mutable_data<T>(context.GetPlace());
-
-    auto x = framework::EigenVector<T>::Flatten(*X);
-    auto y = framework::EigenVector<T>::Flatten(*Y);
-    auto dy = framework::EigenVector<T>::Flatten(*dY);
-    auto dx = framework::EigenVector<T>::Flatten(*dX);
-    auto place = context.GetEigenDevice<Place>();
+template <typename T>
+struct SoftReluGradFunctor : public BaseActivationFunctor<T> {
+  float threshold;
+  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
+    return {{"threshold", &threshold}};
+  }
+  template <typename Device, typename X, typename Y, typename dY, typename dX>
+  void operator()(Device d, X x, Y y, dY dy, dX dx) const {
     auto temp = ((x > -threshold) * (x < threshold)).template cast<T>().eval();
-    dx.device(place) = dy * (static_cast<T>(1) - (-y).exp()) * temp;
+    dx.device(d) = dy * (static_cast<T>(1) - (-y).exp()) * temp;
   }
 };
 
-template <typename Place, typename T, typename AttrType = T>
-class PowKernel : public framework::OpKernel {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    auto* X = context.Input<framework::Tensor>("X");
-    auto* Y = context.Output<framework::Tensor>("Y");
-    auto factor = static_cast<T>(context.Attr<AttrType>("factor"));
-    Y->mutable_data<T>(context.GetPlace());
-
-    auto x = framework::EigenVector<T>::Flatten(*X);
-    auto y = framework::EigenVector<T>::Flatten(*Y);
-    auto place = context.GetEigenDevice<Place>();
-    y.device(place) = x.pow(factor);
+template <typename T>
+struct PowFunctor : public BaseActivationFunctor<T> {
+  float factor;
+  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
+    return {{"factor", &factor}};
+  }
+  template <typename Device, typename X, typename Y>
+  void operator()(Device d, X x, Y y) const {
+    y.device(d) = x.pow(factor);
   }
 };
 
-template <typename Place, typename T, typename AttrType = T>
-class PowGradKernel : public framework::OpKernel {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    auto* X = context.Input<framework::Tensor>("X");
-    auto* dY = context.Input<framework::Tensor>(framework::GradVarName("Y"));
-    auto* dX = context.Output<framework::Tensor>(framework::GradVarName("X"));
-    auto factor = static_cast<T>(context.Attr<AttrType>("factor"));
-    dX->mutable_data<T>(context.GetPlace());
-
-    auto dy = framework::EigenVector<T>::Flatten(*dY);
-    auto x = framework::EigenVector<T>::Flatten(*X);
-    auto dx = framework::EigenVector<T>::Flatten(*dX);
-    auto place = context.GetEigenDevice<Place>();
-
-    dx.device(place) = dy * factor * x.pow(factor - static_cast<T>(1));
+template <typename T>
+struct PowGradFunctor : public BaseActivationFunctor<T> {
+  float factor;
+  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
+    return {{"factor", &factor}};
+  }
+  template <typename Device, typename X, typename Y, typename dY, typename dX>
+  void operator()(Device d, X x, Y y, dY dy, dX dx) const {
+    dx.device(d) = dy * factor * x.pow(factor - static_cast<T>(1));
   }
 };
 
-template <typename Place, typename T, typename AttrType = T>
-class STanhKernel : public framework::OpKernel {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    auto* X = context.Input<framework::Tensor>("X");
-    auto* Y = context.Output<framework::Tensor>("Y");
-    auto scale_a = static_cast<T>(context.Attr<AttrType>("scale_a"));
-    auto scale_b = static_cast<T>(context.Attr<AttrType>("scale_b"));
-    Y->mutable_data<T>(context.GetPlace());
+template <typename T>
+struct STanhFunctor : public BaseActivationFunctor<T> {
+  float scale_a;
+  float scale_b;
+  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
+    return {{"scale_a", &scale_a}, {"scale_b", &scale_b}};
+  }
 
-    auto x = framework::EigenVector<T>::Flatten(*X);
-    auto y = framework::EigenVector<T>::Flatten(*Y);
-    auto place = context.GetEigenDevice<Place>();
-    y.device(place) = scale_b * (scale_a * x).tanh();
+  template <typename Device, typename X, typename Y>
+  void operator()(Device d, X x, Y y) const {
+    y.device(d) = scale_b * (scale_a * x).tanh();
   }
 };
 
-template <typename Place, typename T, typename AttrType = T>
-class STanhGradKernel : public framework::OpKernel {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    auto* X = context.Input<framework::Tensor>("X");
-    auto* dY = context.Input<framework::Tensor>(framework::GradVarName("Y"));
-    auto* dX = context.Output<framework::Tensor>(framework::GradVarName("X"));
-    auto scale_a = static_cast<T>(context.Attr<AttrType>("scale_a"));
-    auto scale_b = static_cast<T>(context.Attr<AttrType>("scale_b"));
-    dX->mutable_data<T>(context.GetPlace());
-
-    auto dy = framework::EigenVector<T>::Flatten(*dY);
-    auto x = framework::EigenVector<T>::Flatten(*X);
-    auto dx = framework::EigenVector<T>::Flatten(*dX);
-    auto place = context.GetEigenDevice<Place>();
+template <typename T>
+struct STanhGradFunctor : public BaseActivationFunctor<T> {
+  float scale_a;
+  float scale_b;
+  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
+    return {{"scale_a", &scale_a}, {"scale_b", &scale_b}};
+  }
 
+  template <typename Device, typename X, typename Y, typename dY, typename dX>
+  void operator()(Device d, X x, Y y, dY dy, dX dx) const {
     auto temp = (scale_a * x).tanh() * (scale_a * x).tanh();
-    dx.device(place) = dy * scale_a * scale_b * (static_cast<T>(1) - temp);
+    dx.device(d) = dy * scale_a * scale_b * (static_cast<T>(1) - temp);
   }
 };
 
 }  // namespace operators
 }  // namespace paddle
+
+#define FOR_EACH_KERNEL_FUNCTOR(__macro)                         \
+  __macro(sigmoid, SigmoidFunctor, SigmoidGradFunctor);          \
+  __macro(exp, ExpFunctor, ExpGradFunctor);                      \
+  __macro(relu, ReluFunctor, ReluGradFunctor);                   \
+  __macro(tanh, TanhFunctor, TanhGradFunctor);                   \
+  __macro(sqrt, SqrtFunctor, SqrtGradFunctor);                   \
+  __macro(abs, AbsFunctor, AbsGradFunctor);                      \
+  __macro(reciprocal, ReciprocalFunctor, ReciprocalGradFunctor); \
+  __macro(log, LogFunctor, LogGradFunctor);                      \
+  __macro(square, SquareFunctor, SquareGradFunctor);             \
+  __macro(brelu, BReluFunctor, BReluGradFunctor);                \
+  __macro(soft_relu, SoftReluFunctor, SoftReluGradFunctor);      \
+  __macro(pow, PowFunctor, PowGradFunctor);                      \
+  __macro(stanh, STanhFunctor, STanhGradFunctor);                \
+  __macro(softsign, SoftsignFunctor, SoftsignGradFunctor)

paddle/operators/add_op.h

@@ -25,7 +25,7 @@ template <typename T, int MajorType = Eigen::RowMajor,
 using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
 
 template <typename Place, typename T>
-class AddKernel : public framework::OpKernel {
+class AddKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto* input0 = context.Input<Tensor>("X");

paddle/operators/clip_op.h

@@ -56,7 +56,7 @@ class ClipGradFunctor {
 };
 
 template <typename Place, typename T>
-class ClipKernel : public framework::OpKernel {
+class ClipKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto max = context.Attr<T>("max");
@@ -73,7 +73,7 @@ class ClipKernel : public framework::OpKernel {
 };
 
 template <typename Place, typename T>
-class ClipGradKernel : public framework::OpKernel {
+class ClipGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto max = context.Attr<T>("max");

paddle/operators/concat_op.h

@@ -22,7 +22,7 @@ namespace paddle {
 namespace operators {
 
 template <typename Place, typename T>
-class ConcatKernel : public framework::OpKernel {
+class ConcatKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto ins = ctx.MultiInput<framework::Tensor>("X");
@@ -44,7 +44,7 @@ class ConcatKernel : public framework::OpKernel {
 };
 
 template <typename Place, typename T>
-class ConcatGradKernel : public framework::OpKernel {
+class ConcatGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const {
     auto* in = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));

paddle/operators/cond_op.cc

@@ -14,12 +14,7 @@ limitations under the License. */
 
 #include "paddle/operators/cond_op.h"
 
-#include <cstring>
-#include <sstream>
-
-#include "paddle/framework/op_registry.h"
 #include "paddle/operators/gather.h"
-#include "paddle/operators/net_op.h"
 #include "paddle/operators/scatter.h"
 
 namespace paddle {
@@ -31,142 +26,104 @@ using Tensor = framework::Tensor;
 using LoDTensor = framework::LoDTensor;
 using DDim = framework::DDim;
 
-void CondOp::CreateScope(const Scope& scope) const {
+framework::Scope& CondOp::AddSubScope(const Scope& scope) const {
   auto sub_scopes_var = scope.FindVar("SubScopes");
   PADDLE_ENFORCE_NOT_NULL(sub_scopes_var,
                           "Output(SubScopes) of CondOp should not be null.");
   auto sub_scopes = sub_scopes_var->GetMutable<std::vector<Scope*>>();
   auto& sub_scope = scope.NewScope();
   sub_scopes->push_back(&sub_scope);
+  return sub_scope;
 }
 
-void CondOp::CreateIndexTensor(const Scope& scope) const {
+std::vector<framework::Scope*>& CondOp::GetSubScopes(
+    const framework::Scope& scope) const {
+  auto sub_scopes_var = scope.FindVar("SubScopes");
+  PADDLE_ENFORCE_NOT_NULL(sub_scopes_var,
+                          "Output(SubScopes) of CondOp should not be null.");
+  return *sub_scopes_var->GetMutable<std::vector<framework::Scope*>>();
+}
+
+LoDTensor& CondOp::AddIndexTensor(const Scope& scope) const {
   auto index_tensors_var = scope.FindVar("IndexTensors");
   PADDLE_ENFORCE_NOT_NULL(index_tensors_var,
                           "Output(IndexTensors) of CondOp should not be null.");
   auto& index_tensors =
       *index_tensors_var->GetMutable<std::vector<LoDTensor>>();
   index_tensors.push_back(LoDTensor());
+  return index_tensors.back();
 }
 
-void CondOp::InferShape(const Scope& scope) const {
-  auto sub_scopes_var = scope.FindVar("SubScopes");
-  PADDLE_ENFORCE_NOT_NULL(sub_scopes_var,
-                          "Output(SubScopes) of CondOp should not be null.");
-  auto& sub_scopes = *sub_scopes_var->GetMutable<std::vector<Scope*>>();
-
-  for (int i = 0; i < 2; ++i) {
-    // Create two sub scopes for true and false branches
-    // sub_scopes[0] for the true branch and sub_scopes[1] for the false
-    // branch
-    CreateScope(scope);
-
-    // Create two tensors for true and false indices
-    // index_tensors[0] for the true branch and index_tensors[1] for the false
-    // branch
-    CreateIndexTensor(scope);
-
-    PADDLE_ENFORCE(!Inputs("Xs").empty(),
-                   "Inputs(Xs) of CondOp can't be empty.");
-    for (auto& input : Inputs("Xs")) {
-      // Create a new tensor in sub-scope for input-type tensor
-      Variable* v = sub_scopes[i]->NewVar(input);
-      LoDTensor* sub_input = v->GetMutable<LoDTensor>();
-      sub_input->Resize(scope.FindVar(input)->GetMutable<LoDTensor>()->dims());
-    }
-
-    for (auto& output : (*sub_net_op_[i]).Outputs()) {
-      for (auto& var_name : output.second) {
-        sub_scopes[i]->NewVar(var_name);
-      }
-    }
-
-    // each net calls InferShape
-    //    sub_net_op_[i]->InferShape(*sub_scopes[i]);
-  }
-
-  for (auto& output : Outputs("Outs")) {
-    LoDTensor* tensor_t_out =
-        sub_scopes[0]->FindVar(output)->GetMutable<LoDTensor>();
-    PADDLE_ENFORCE_NOT_NULL(tensor_t_out, "True output should not be NULL");
-    LoDTensor* tensor_f_out =
-        sub_scopes[1]->FindVar(output)->GetMutable<LoDTensor>();
-    PADDLE_ENFORCE_NOT_NULL(tensor_f_out, "False output should not be NULL");
-
-    auto* tensor_out_var = scope.FindVar(output);
-    PADDLE_ENFORCE_NOT_NULL(tensor_out_var, "Output not found");
-    LoDTensor* tensor_out = tensor_out_var->GetMutable<LoDTensor>();
-    PADDLE_ENFORCE_NOT_NULL(tensor_t_out,
-                            "True output tensor should not be NULL");
-
-    // check output size should be same
-    PADDLE_ENFORCE_EQ(tensor_t_out->dims(), tensor_f_out->dims(),
-                      "Outputs not of the same shape");
-    tensor_out->Resize(tensor_t_out->dims());
-    // tensor_out->mutable_data<float>(tensor_out->dims(),
-    // platform::CPUPlace());
-    tensor_out->mutable_data<float>(platform::CPUPlace());
-  }
-}
-
-void CondOp::Run(const Scope& scope,
-                 const platform::DeviceContext& dev_ctx) const {
-  auto* sub_scopes_var = scope.FindVar("SubScopes");
-  PADDLE_ENFORCE_NOT_NULL(sub_scopes_var,
-                          "Output(SubScopes) of CondOp should not be null.");
-  auto sub_scopes = sub_scopes_var->Get<std::vector<Scope*>>();
+std::vector<framework::LoDTensor>& CondOp::GetIndexTensors(
+    const framework::Scope& scope) const {
   auto* index_tensors_var = scope.FindVar("IndexTensors");
   PADDLE_ENFORCE_NOT_NULL(index_tensors_var,
                           "Output(IndexTensors) of CondOp should not be null.");
-  auto index_tensors = index_tensors_var->Get<std::vector<LoDTensor>>();
+  return *index_tensors_var->GetMutable<std::vector<framework::LoDTensor>>();
+}
 
-  std::string cond_name = Input("Cond");
-  Variable* cond_var = scope.FindVar(cond_name);
+void CondOp::PrepareDataForSubnet(
+    const framework::Scope& scope,
+    const platform::DeviceContext& dev_ctx) const {
+  PADDLE_ENFORCE(!Inputs("Xs").empty(), "Inputs(Xs) of CondOp can't be empty.");
+
+  for (int i = 0; i < BRANCH_NUM; ++i) {
+    // Create two sub scopes for true and false branches
+    //   sub_scopes[0] for the true branch
+    //   sub_scopes[1] for the false branch
+    AddSubScope(scope);
+    // Create two tensors for true and false indices:
+    //   index_tensors[0] for the true branch
+    //   index_tensors[1] for the false branch
+    AddIndexTensor(scope);
+  }
+
+  Variable* cond_var = scope.FindVar(Input("Cond"));
   PADDLE_ENFORCE_NOT_NULL(cond_var,
                           "Input(Cond) of CondOp should not be null.");
   const LoDTensor* cond = cond_var->GetMutable<LoDTensor>();
 
-  // Step 1: get the true/false index at runtime
-  // index_[0]: vector<int>, contains all index for cond[i] == true
-  // index_[1]: vector<int>, contains all index for cond[i] == false
-  for (int i = 0; i < 2; ++i) index_[i].clear();
+  // get the true/false index at runtime according to cond tensor
+  // index_vectors[0]: vector<int>, contains all index for cond[i] == true
+  // index_vectors[1]: vector<int>, contains all index for cond[i] == false
+  std::vector<std::vector<int>> index_vectors;
+  index_vectors.resize(BRANCH_NUM);
 
   const int* cond_data = cond->data<int>();
   for (int i = 0; i < cond->dims()[0]; ++i) {
     if (cond_data[i])
-      index_[0].push_back(i);
+      index_vectors[TRUE_BRANCH].push_back(i);
     else
-      index_[1].push_back(i);
+      index_vectors[FALSE_BRANCH].push_back(i);
   }
 
-  // put index_[0] and index_[1] into two tensors:
-  // index_tensor_[0] and index_tensor_[1]
-  DDim dim = paddle::framework::make_ddim({0});
-  for (int i = 0; i < 2; ++i) {
-    dim[0] = index_[i].size();
-    int* tmp_ptr =
+  // put index_vectors[0] and index_vectors[1] into two tensors:
+  // index_tensors[0] and index_tensors[1]
+  std::vector<framework::LoDTensor>& index_tensors = GetIndexTensors(scope);
+  std::vector<framework::Scope*>& sub_scopes = GetSubScopes(scope);
+
+  for (int i = 0; i < BRANCH_NUM; ++i) {
+    DDim dim = {static_cast<int64_t>(index_vectors[i].size())};
+    int* index_tensor_data_ptr =
         index_tensors[i].mutable_data<int>(dim, platform::CPUPlace());
-    index_tensors[i].Resize(dim);
-    memcpy(tmp_ptr, index_[i].data(), dim[0] * sizeof(int));
+    memcpy(index_tensor_data_ptr, index_vectors[i].data(),
+           dim[0] * sizeof(int));
   }
 
-  // Step 2: collect data by calling gather
-  for (int i = 0; i < 2; ++i) {
-    // i= 0/i for True and False branches respectively
-    for (auto& input : Inputs("Xs")) {
-      // find Tensor
-      Variable* v = scope.FindVar(input);
-      PADDLE_ENFORCE_NOT_NULL(v);
-      LoDTensor* tensor_parent = v->GetMutable<LoDTensor>();
+  // create input in subscopes according to index_vectors
+  for (auto& input : Inputs("Xs")) {
+    Variable* var_parent = scope.FindVar(input);
+    PADDLE_ENFORCE_NOT_NULL(var_parent);
+    const auto* tensor_parent = &var_parent->Get<LoDTensor>();
 
-      v = sub_scopes[i]->FindVar(input);
-      PADDLE_ENFORCE_NOT_NULL(v);
-      LoDTensor* tensor_child = v->GetMutable<LoDTensor>();
+    for (int i = 0; i < BRANCH_NUM; ++i) {
+      Variable* var_child = sub_scopes[i]->FindVar(input);
+      PADDLE_ENFORCE_NOT_NULL(var_child);
+      auto* tensor_child = var_child->GetMutable<LoDTensor>();
 
       // Resize child
-      DDim dim = tensor_child->dims();
-      dim[0] = index_[i].size();
-      tensor_child->Resize(dim);
+      DDim dim = tensor_parent->dims();
+      dim[0] = index_tensors[i].dims()[0];
       tensor_child->mutable_data<float>(dim, platform::CPUPlace());
 
       Gather<float>(dev_ctx.GetPlace(), tensor_parent, &index_tensors[i],
@@ -174,32 +131,79 @@ void CondOp::Run(const Scope& scope,
     }
   }
 
-  // Step 3: run
-  for (int i = 0; i < 2; ++i) {
-    sub_net_op_[i]->Run(*sub_scopes[i], dev_ctx);
+  // create output_tensors in subscope for sub_net
+  for (int i = 0; i < BRANCH_NUM; ++i) {
+    for (auto& output : (*sub_net_op_[i]).Outputs()) {
+      for (auto& var_name : output.second) {
+        sub_scopes[i]->NewVar(var_name);
+      }
+    }
   }
+}
 
-  // Step 4: merge output results
+void CondOp::MergeDataFromSubnet(const framework::Scope& scope,
+                                 const platform::DeviceContext& dev_ctx) const {
+  std::vector<framework::Scope*>& sub_scopes = GetSubScopes(scope);
+  const std::vector<framework::LoDTensor>& index_tensors =
+      GetIndexTensors(scope);
+
+  // Infer the output dim, out_dim[0] = true_dim[0] + false_dim[0]
   PADDLE_ENFORCE(!Outputs("Outs").empty(),
                  "Outputs(Outs) of CondOp can't be empty.");
-  for (int i = 0; i < 2; ++i) {
-    // i= 0/i for True and False branches respectively
-    for (auto& output : Outputs("Outs")) {
-      // find Tensor
-      Variable* v = scope.FindVar(output);
-      PADDLE_ENFORCE_NOT_NULL(v);
-      LoDTensor* tensor_parent = v->GetMutable<LoDTensor>();
-
-      v = sub_scopes[i]->FindVar(output);
-      PADDLE_ENFORCE_NOT_NULL(v);
-      LoDTensor* tensor_child = v->GetMutable<LoDTensor>();
+  for (auto& output : Outputs("Outs")) {
+    const LoDTensor* tensor_t_out =
+        &sub_scopes[TRUE_BRANCH]->FindVar(output)->Get<LoDTensor>();
+    PADDLE_ENFORCE_NOT_NULL(tensor_t_out, "True output should not be NULL");
+    const LoDTensor* tensor_f_out =
+        &sub_scopes[FALSE_BRANCH]->FindVar(output)->Get<LoDTensor>();
+    PADDLE_ENFORCE_NOT_NULL(tensor_f_out, "False output should not be NULL");
+
+    auto* var_out = scope.FindVar(output);
+    PADDLE_ENFORCE_NOT_NULL(var_out, "Output not found");
+    LoDTensor* tensor_out = var_out->GetMutable<LoDTensor>();
+    PADDLE_ENFORCE_NOT_NULL(tensor_t_out,
+                            "True output tensor should not be NULL");
+
+    DDim true_dim = tensor_t_out->dims();
+    DDim false_dim = tensor_f_out->dims();
+    true_dim[0] = 0;
+    false_dim[0] = 0;
+    PADDLE_ENFORCE_EQ(true_dim, false_dim,
+                      "Outputs not of the same shape except the first dim");
+
+    DDim out_dim = tensor_t_out->dims();
+    out_dim[0] = tensor_t_out->dims()[0] + tensor_f_out->dims()[0];
+    tensor_out->Resize(out_dim);
+    tensor_out->mutable_data<float>(platform::CPUPlace());
+  }
 
+  // merge output results:
+  // output_tensor = true_output_tensor + false_output_tensor
+  for (auto& output : Outputs("Outs")) {
+    Variable* var_parent = scope.FindVar(output);
+    PADDLE_ENFORCE_NOT_NULL(var_parent);
+    auto* tensor_parent = var_parent->GetMutable<LoDTensor>();
+
+    for (int i = 0; i < BRANCH_NUM; ++i) {
+      Variable* var_child = sub_scopes[i]->FindVar(output);
+      PADDLE_ENFORCE_NOT_NULL(var_child);
+      auto* tensor_child = &var_child->Get<LoDTensor>();
       ScatterUpdate<float>(dev_ctx.GetPlace(), tensor_child, &index_tensors[i],
                            tensor_parent);
     }
   }
 }
 
+void CondOp::Run(const Scope& scope,
+                 const platform::DeviceContext& dev_ctx) const {
+  PrepareDataForSubnet(scope, dev_ctx);
+  std::vector<framework::Scope*>& sub_scopes = GetSubScopes(scope);
+  for (int i = 0; i < BRANCH_NUM; ++i) {
+    sub_net_op_[i]->Run(*sub_scopes[i], dev_ctx);
+  }
+  MergeDataFromSubnet(scope, dev_ctx);
+}
+
 class CondOpProtoAndCheckerMaker : public framework::OpProtoAndCheckerMaker {
  public:
   CondOpProtoAndCheckerMaker(framework::OpProto* proto,

paddle/operators/cond_op.h

@@ -40,8 +40,7 @@ class CondOp : public framework::OperatorBase {
          const framework::VariableNameMap& outputs,
          const framework::AttributeMap& attrs)
       : OperatorBase(type, inputs, outputs, attrs) {
-    index_.resize(2);
-    sub_net_op_.resize(2);
+    sub_net_op_.resize(BRANCH_NUM);
   }
 
   CondOp(const CondOp& o)
@@ -51,42 +50,44 @@ class CondOp : public framework::OperatorBase {
     PADDLE_THROW("Not implemented");
   }
 
-  void CreateScope(const framework::Scope& scope) const;
+  framework::Scope& AddSubScope(const framework::Scope& scope) const;
+  std::vector<framework::Scope*>& GetSubScopes(
+      const framework::Scope& scope) const;
 
-  void CreateIndexTensor(const framework::Scope& scope) const;
+  framework::LoDTensor& AddIndexTensor(const framework::Scope& scope) const;
+  std::vector<framework::LoDTensor>& GetIndexTensors(
+      const framework::Scope& scope) const;
 
-  /*
-   * InferShape must be called before Run.
-   * FIXME(yuyang18): Since InferShape has been removed, this implementation
-   * could be wrong.
-   */
-  void InferShape(const framework::Scope& scope) const;
+  void PrepareDataForSubnet(const framework::Scope& scope,
+                            const platform::DeviceContext& dev_ctx) const;
+  void MergeDataFromSubnet(const framework::Scope& scope,
+                           const platform::DeviceContext& dev_ctx) const;
 
   /*
    * Set True Block
    */
   void set_truenet(std::unique_ptr<OperatorBase>&& net) {
-    sub_net_op_[0] = std::move(net);
+    sub_net_op_[TRUE_BRANCH] = std::move(net);
   }
 
   /*
    * Set False Block
    */
   void set_falsenet(std::unique_ptr<OperatorBase>&& net) {
-    sub_net_op_[1] = std::move(net);
+    sub_net_op_[FALSE_BRANCH] = std::move(net);
   }
 
   void Run(const framework::Scope& scope,
            const platform::DeviceContext& dev_ctx) const override;
 
  private:
+  const int TRUE_BRANCH = 0;
+  const int FALSE_BRANCH = 1;
+  const int BRANCH_NUM = 2;
+
   // sub_net_op_[0]: subnet_t
   // sub_net_op_[1]: subnet_f
   std::vector<std::unique_ptr<framework::OperatorBase>> sub_net_op_;
-
-  // index_[0]: True_index;
-  // index_[1]: False_index;
-  mutable std::vector<std::vector<int>> index_;
 };
 
 }  // namespace operators

paddle/operators/cos_sim_op.h

@@ -28,7 +28,7 @@ template <typename T, int MajorType = Eigen::RowMajor,
 using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
 
 template <typename Place, typename T>
-class CosSimKernel : public framework::OpKernel {
+class CosSimKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     // get Tensor
@@ -67,7 +67,7 @@ class CosSimKernel : public framework::OpKernel {
 };
 
 template <typename Place, typename T>
-class CosSimGradKernel : public framework::OpKernel {
+class CosSimGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     // get Tensor

paddle/operators/crop_op.h

@@ -27,7 +27,7 @@ using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
 using framework::Tensor;
 
 template <typename T>
-class CropKernel : public framework::OpKernel {
+class CropKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto* x = context.Input<Tensor>("X");
@@ -69,7 +69,7 @@ void CropGradFunction(const framework::ExecutionContext& context) {
 }
 
 template <typename Place, typename T>
-class CropGradKernel : public framework::OpKernel {
+class CropGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     size_t rank =

paddle/operators/cross_entropy_op.cc

@@ -47,6 +47,12 @@ class CrossEntropyOp : public framework::OperatorWithKernel {
     ctx->SetOutputDim("Y", {x_dims[0], 1});
     ctx->ShareLoD("X", /*->*/ "Y");
   }
+
+  // CrossEntropy's data type just determined by "X"
+  framework::DataType IndicateDataType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::ToDataType(ctx.Input<Tensor>("X")->type());
+  }
 };
 
 class CrossEntropyGradientOp : public framework::OperatorWithKernel {
@@ -87,6 +93,12 @@ class CrossEntropyGradientOp : public framework::OperatorWithKernel {
     }
     ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
   }
+
+  // CrossEntropy's data type just determined by "X"
+  framework::DataType IndicateDataType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::ToDataType(ctx.Input<Tensor>("X")->type());
+  }
 };
 
 class CrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker {

paddle/operators/cross_entropy_op.cu

@@ -18,14 +18,6 @@ namespace paddle {
 namespace operators {
 
 namespace {
-// TODO(qingqing): make zero setting a common function.
-template <typename T>
-__global__ void Zero(T* X, const int N) {
-  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N;
-       i += blockDim.x * gridDim.x) {
-    X[i] = 0.0;
-  }
-}
 
 template <typename T>
 __global__ void CrossEntropyGradientKernel(T* dX, const T* dY, const T* X,
@@ -53,7 +45,7 @@ __global__ void SoftCrossEntropyGradientKernel(T* dX, const T* dY, const T* X,
 }  // namespace
 
 template <typename T>
-class CrossEntropyOpCUDAKernel : public framework::OpKernel {
+class CrossEntropyOpCUDAKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
@@ -64,12 +56,12 @@ class CrossEntropyOpCUDAKernel : public framework::OpKernel {
     y->mutable_data<T>(ctx.GetPlace());
 
     math::CrossEntropyFunctor<platform::GPUPlace, T>()(
-        ctx, y, x, label, ctx.Attr<bool>("softLabel"));
+        ctx.device_context(), y, x, label, ctx.Attr<bool>("softLabel"));
   }
 };
 
 template <typename T>
-class CrossEntropyGradientOpCUDAKernel : public framework::OpKernel {
+class CrossEntropyGradientOpCUDAKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
@@ -99,11 +91,7 @@ class CrossEntropyGradientOpCUDAKernel : public framework::OpKernel {
                               .stream()>>>(dx_data, dy_data, x_data, label_data,
                                            batch_size, class_num);
     } else {
-      Zero<T><<<grid, block, 0,
-                reinterpret_cast<const platform::CUDADeviceContext&>(
-                    ctx.device_context())
-                    .stream()>>>(dx_data, batch_size * class_num);
-
+      math::SetConstant<platform::GPUPlace, T>(ctx.device_context(), dx, 0);
       auto* label_data = label->data<int>();
       grid = (batch_size + block - 1) / block;
       CrossEntropyGradientKernel<T><<<

paddle/operators/cross_entropy_op.h

@@ -16,6 +16,7 @@ limitations under the License. */
 #include "paddle/framework/eigen.h"
 #include "paddle/framework/op_registry.h"
 #include "paddle/operators/math/cross_entropy.h"
+#include "paddle/operators/math/math_function.h"
 
 namespace paddle {
 namespace operators {
@@ -26,7 +27,7 @@ template <typename T, int MajorType = Eigen::RowMajor,
 using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
 
 template <typename T>
-class CrossEntropyOpKernel : public framework::OpKernel {
+class CrossEntropyOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()),
@@ -37,12 +38,12 @@ class CrossEntropyOpKernel : public framework::OpKernel {
     y->mutable_data<T>(ctx.GetPlace());
 
     math::CrossEntropyFunctor<platform::CPUPlace, T>()(
-        ctx, y, x, labels, ctx.Attr<bool>("softLabel"));
+        ctx.device_context(), y, x, labels, ctx.Attr<bool>("softLabel"));
   }
 };
 
 template <typename T>
-class CrossEntropyGradientOpKernel : public framework::OpKernel {
+class CrossEntropyGradientOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()),
@@ -69,8 +70,7 @@ class CrossEntropyGradientOpKernel : public framework::OpKernel {
       const T* x_data = x->data<T>();
       const int* label_data = label->data<int>();
 
-      // TODO(qingqing): make zero setting a common function.
-      memset(dx_data, 0, sizeof(T) * batch_size * class_num);
+      math::SetConstant<platform::CPUPlace, T>(ctx.device_context(), dx, 0);
 
       for (int i = 0; i < batch_size; ++i) {
         PADDLE_ASSERT(label_data[i] >= 0 || label_data[i] < class_num);

paddle/operators/dropout_op.cu

@@ -47,7 +47,7 @@ struct MaskGenerator {
 // Use std::random and thrust::random(thrust is a std library in CUDA) to
 // implement uniform random.
 template <typename Place, typename T, typename AttrType>
-class GPUDropoutKernel : public framework::OpKernel {
+class GPUDropoutKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto* x = context.Input<Tensor>("X");

paddle/operators/dropout_op.h

@@ -26,7 +26,7 @@ template <typename T, int MajorType = Eigen::RowMajor,
 using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
 
 template <typename Place, typename T, typename AttrType>
-class CPUDropoutKernel : public framework::OpKernel {
+class CPUDropoutKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto* x = context.Input<Tensor>("X");
@@ -62,7 +62,7 @@ class CPUDropoutKernel : public framework::OpKernel {
 };
 
 template <typename Place, typename T>
-class DropoutGradKernel : public framework::OpKernel {
+class DropoutGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     PADDLE_ENFORCE(context.Attr<bool>("is_training"),

paddle/operators/elementwise_add_op.h

@@ -20,7 +20,7 @@ namespace paddle {
 namespace operators {
 
 template <typename Place, typename T>
-class ElementwiseAddKernel : public framework::OpKernel {
+class ElementwiseAddKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     ElementwiseCompute<EigenAddFunctor, Place, T>(ctx);
@@ -101,7 +101,7 @@ struct ElementwiseAddBroadCast2GradFunctor {
 };
 
 template <typename Place, typename T>
-class ElementwiseAddGradKernel : public framework::OpKernel {
+class ElementwiseAddGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     ElementwiseGradCompute<Place, T, ElementwiseAddGradFunctor<T>,

paddle/operators/elementwise_div_op.h

@@ -20,7 +20,7 @@ namespace paddle {
 namespace operators {
 
 template <typename Place, typename T>
-class ElementwiseDivKernel : public framework::OpKernel {
+class ElementwiseDivKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     ElementwiseCompute<EigenDivFunctor, Place, T>(ctx);
@@ -103,7 +103,7 @@ struct ElementwiseDivBroadCast2GradFunctor {
 };
 
 template <typename Place, typename T>
-class ElementwiseDivGradKernel : public framework::OpKernel {
+class ElementwiseDivGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     ElementwiseGradCompute<Place, T, ElementwiseDivGradFunctor<T>,

paddle/operators/elementwise_mul_op.cc

@@ -36,7 +36,9 @@ REGISTER_OP(elementwise_mul, ops::ElementwiseOp, ops::ElementwiseMulOpMaker,
             elementwise_mul_grad, ops::ElementwiseOpGrad);
 REGISTER_OP_CPU_KERNEL(
     elementwise_mul,
-    ops::ElementwiseMulKernel<paddle::platform::CPUPlace, float>);
+    ops::ElementwiseMulKernel<paddle::platform::CPUPlace, float>,
+    ops::ElementwiseMulKernel<paddle::platform::CPUPlace, double>);
 REGISTER_OP_CPU_KERNEL(
     elementwise_mul_grad,
-    ops::ElementwiseMulGradKernel<paddle::platform::CPUPlace, float>);
+    ops::ElementwiseMulGradKernel<paddle::platform::CPUPlace, float>,
+    ops::ElementwiseMulGradKernel<paddle::platform::CPUPlace, double>);

paddle/operators/elementwise_mul_op.cu

@@ -19,7 +19,9 @@ namespace ops = paddle::operators;
 
 REGISTER_OP_GPU_KERNEL(
     elementwise_mul,
-    ops::ElementwiseMulKernel<paddle::platform::GPUPlace, float>);
+    ops::ElementwiseMulKernel<paddle::platform::GPUPlace, float>,
+    ops::ElementwiseMulKernel<paddle::platform::GPUPlace, double>);
 REGISTER_OP_GPU_KERNEL(
     elementwise_mul_grad,
-    ops::ElementwiseMulGradKernel<paddle::platform::GPUPlace, float>);
+    ops::ElementwiseMulGradKernel<paddle::platform::GPUPlace, float>,
+    ops::ElementwiseMulGradKernel<paddle::platform::GPUPlace, double>);

paddle/operators/elementwise_mul_op.h

@@ -19,7 +19,7 @@ namespace paddle {
 namespace operators {
 
 template <typename Place, typename T>
-class ElementwiseMulKernel : public framework::OpKernel {
+class ElementwiseMulKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     ElementwiseCompute<EigenMulFunctor, Place, T>(ctx);
@@ -102,7 +102,7 @@ struct ElementwiseMulBroadCast2GradFunctor {
 };
 
 template <typename Place, typename T>
-class ElementwiseMulGradKernel : public framework::OpKernel {
+class ElementwiseMulGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     ElementwiseGradCompute<Place, T, ElementwiseMulGradFunctor<T>,

paddle/operators/elementwise_sub_op.h

@@ -19,7 +19,7 @@ namespace paddle {
 namespace operators {
 
 template <typename Place, typename T>
-class ElementwiseSubKernel : public framework::OpKernel {
+class ElementwiseSubKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     ElementwiseCompute<EigenSubFunctor, Place, T>(ctx);
@@ -102,7 +102,7 @@ struct ElementwiseSubBroadCast2GradFunctor {
 };
 
 template <typename Place, typename T>
-class ElementwiseSubGradKernel : public framework::OpKernel {
+class ElementwiseSubGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     ElementwiseGradCompute<Place, T, ElementwiseSubGradFunctor<T>,

paddle/operators/fc_op.cc

@@ -100,7 +100,7 @@ class FCOp : public NetOp {
 
       add_out = Output("AddOut");
       AppendOp(framework::OpRegistry::CreateOp(
-          "rowwise_add", {{"X", {sum_out}}, {"b", {Input("B")}}},
+          "elementwise_add", {{"X", {sum_out}}, {"Y", {Input("B")}}},
           {{"Out", {add_out}}}, {}));
     } else {
       if (Output("AddOut") != framework::kEmptyVarName) {

paddle/operators/fill_zeros_like_op.h

@@ -20,7 +20,7 @@ namespace paddle {
 namespace operators {
 
 template <typename Place, typename T>
-class FillZerosLikeKernel : public framework::OpKernel {
+class FillZerosLikeKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto* output = context.Output<framework::Tensor>("Y");

paddle/operators/gather_op.cc

@@ -37,6 +37,11 @@ class GatherOp : public framework::OperatorWithKernel {
     output_dims[0] = batch_size;
     ctx->SetOutputDim("Out", output_dims);
   }
+
+  framework::DataType IndicateDataType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::ToDataType(ctx.Input<Tensor>("X")->type());
+  }
 };
 
 class GatherGradOp : public framework::OperatorWithKernel {
@@ -47,6 +52,11 @@ class GatherGradOp : public framework::OperatorWithKernel {
   void InferShape(framework::InferShapeContextBase* ctx) const override {
     ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
   }
+
+  framework::DataType IndicateDataType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::ToDataType(ctx.Input<Tensor>("X")->type());
+  }
 };
 
 class GatherOpMaker : public framework::OpProtoAndCheckerMaker {

paddle/operators/gather_op.h

@@ -24,7 +24,7 @@ namespace operators {
 using Tensor = framework::Tensor;
 
 template <typename Place, typename T>
-class GatherOpKernel : public framework::OpKernel {
+class GatherOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext &ctx) const override {
     auto *X = ctx.Input<Tensor>("X");
@@ -37,7 +37,7 @@ class GatherOpKernel : public framework::OpKernel {
 };
 
 template <typename Place, typename T>
-class GatherGradientOpKernel : public framework::OpKernel {
+class GatherGradientOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext &ctx) const override {
     auto *Index = ctx.Input<Tensor>("Index");

paddle/operators/gaussian_random_op.cc

@@ -16,7 +16,7 @@ namespace paddle {
 namespace operators {
 
 template <typename T>
-class CPUGaussianRandomKernel : public framework::OpKernel {
+class CPUGaussianRandomKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     float mean = context.Attr<float>("mean");
@@ -56,6 +56,11 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
                    "dims can be one int or array. dims must be set.");
     ctx->SetOutputDim("Out", framework::make_ddim(temp));
   }
+
+  framework::DataType IndicateDataType(
+      const framework::ExecutionContext& ctx) const override {
+    return static_cast<framework::DataType>(Attr<int>("data_type"));
+  }
 };
 
 class GaussianRandomOpMaker : public framework::OpProtoAndCheckerMaker {
@@ -76,6 +81,8 @@ Use to initialize tensor with gaussian random generator.
                  "Random seed of generator."
                  "0 means use system wide seed")
         .SetDefault(0);
+    AddAttr<int>("data_type", "output data type")
+        .SetDefault(framework::DataType::FP32);
   }
 };
 

paddle/operators/gaussian_random_op.cu

@@ -37,7 +37,7 @@ struct GaussianGenerator {
 };
 
 template <typename T>
-class GPUGaussianRandomKernel : public framework::OpKernel {
+class GPUGaussianRandomKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto* tensor = context.Output<framework::Tensor>("Out");

paddle/operators/gemm_conv2d_op.h

@@ -25,7 +25,7 @@ namespace operators {
 using Tensor = framework::Tensor;
 
 template <typename Place, typename T>
-class GemmConv2DKernel : public framework::OpKernel {
+class GemmConv2DKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     const Tensor* input = context.Input<Tensor>("Input");
@@ -98,7 +98,7 @@ class GemmConv2DKernel : public framework::OpKernel {
 };
 
 template <typename Place, typename T>
-class GemmConvGrad2DKernel : public framework::OpKernel {
+class GemmConvGrad2DKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     const Tensor* input = context.Input<Tensor>("Input");

paddle/operators/lookup_table_op.cc

@@ -36,6 +36,11 @@ class LookupTableOp : public framework::OperatorWithKernel {
     ctx->SetOutputDim("Out", {ids_dims[0], table_dims[1]});
     ctx->ShareLoD("Ids", /*->*/ "Out");
   }
+
+  framework::DataType IndicateDataType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::ToDataType(ctx.Input<Tensor>("W")->type());
+  }
 };
 
 class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
@@ -69,6 +74,11 @@ class LookupTableOpGrad : public framework::OperatorWithKernel {
     auto table_dims = ctx->GetInputDim("W");
     ctx->SetOutputDim(framework::GradVarName("W"), table_dims);
   }
+
+  framework::DataType IndicateDataType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::ToDataType(ctx.Input<Tensor>("W")->type());
+  }
 };
 
 }  // namespace operators

paddle/operators/lookup_table_op.cu

@@ -61,7 +61,7 @@ __global__ void LookupTableGrad(T* table, const T* output, const int32_t* ids,
 }
 
 template <typename T>
-class LookupTableCUDAKernel : public framework::OpKernel {
+class LookupTableCUDAKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto table_t = context.Input<Tensor>("W");
@@ -85,7 +85,7 @@ class LookupTableCUDAKernel : public framework::OpKernel {
 };
 
 template <typename T>
-class LookupTableGradCUDAKernel : public framework::OpKernel {
+class LookupTableGradCUDAKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto ids_t = context.Input<Tensor>("Ids");

paddle/operators/lookup_table_op.h

@@ -23,7 +23,7 @@ namespace operators {
 using Tensor = framework::Tensor;
 
 template <typename T>
-class LookupTableKernel : public framework::OpKernel {
+class LookupTableKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto table_t = context.Input<Tensor>("W");      // float tensor
@@ -44,7 +44,7 @@ class LookupTableKernel : public framework::OpKernel {
 };
 
 template <typename T>
-class LookupTableGradKernel : public framework::OpKernel {
+class LookupTableGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto ids_t = context.Input<Tensor>("Ids");

paddle/operators/lstm_unit_op.cc

@@ -47,7 +47,6 @@ class LstmUnitOp : public framework::OperatorWithKernel {
   }
 };
 
-template <typename AttrType>
 class LstmUnitOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   LstmUnitOpMaker(framework::OpProto* proto,
@@ -68,7 +67,7 @@ Equation:
   H = C * sigm(o)
 
 )DOC");
-    AddAttr<AttrType>("forget_bias", "The forget bias of Lstm Unit.")
+    AddAttr<float>("forget_bias", "The forget bias of Lstm Unit.")
         .SetDefault(0.0);
   }
 };
@@ -93,9 +92,11 @@ class LstmUnitGradOp : public framework::OperatorWithKernel {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(lstm_unit, ops::LstmUnitOp, ops::LstmUnitOpMaker<float>,
-            lstm_unit_grad, ops::LstmUnitGradOp);
+REGISTER_OP(lstm_unit, ops::LstmUnitOp, ops::LstmUnitOpMaker, lstm_unit_grad,
+            ops::LstmUnitGradOp);
 REGISTER_OP_CPU_KERNEL(lstm_unit,
-                       ops::LstmUnitKernel<paddle::platform::CPUPlace, float>);
+                       ops::LstmUnitKernel<paddle::platform::CPUPlace, float>,
+                       ops::LstmUnitKernel<paddle::platform::CPUPlace, double>);
 REGISTER_OP_CPU_KERNEL(
-    lstm_unit_grad, ops::LstmUnitGradKernel<paddle::platform::CPUPlace, float>);
+    lstm_unit_grad, ops::LstmUnitGradKernel<paddle::platform::CPUPlace, float>,
+    ops::LstmUnitGradKernel<paddle::platform::CPUPlace, double>);

paddle/operators/lstm_unit_op.cu

@@ -89,8 +89,8 @@ __global__ void LSTMUnitGradientKernel(const int nthreads, const int dim,
   }
 }
 
-template <typename T, typename AttrType = T>
-class LstmUnitOpCUDAKernel : public framework::OpKernel {
+template <typename T>
+class LstmUnitOpCUDAKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
@@ -101,7 +101,7 @@ class LstmUnitOpCUDAKernel : public framework::OpKernel {
     auto* c_tensor = ctx.Output<framework::Tensor>("C");
     auto* h_tensor = ctx.Output<framework::Tensor>("H");
 
-    auto forget_bias = static_cast<T>(ctx.Attr<AttrType>("forget_bias"));
+    auto forget_bias = static_cast<T>(ctx.Attr<float>("forget_bias"));
 
     int b_size = c_tensor->dims()[0];
     int D = c_tensor->dims()[1];
@@ -120,8 +120,8 @@ class LstmUnitOpCUDAKernel : public framework::OpKernel {
   }
 };
 
-template <typename T, typename AttrType = T>
-class LstmUnitGradOpCUDAKernel : public framework::OpKernel {
+template <typename T>
+class LstmUnitGradOpCUDAKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
@@ -153,7 +153,7 @@ class LstmUnitGradOpCUDAKernel : public framework::OpKernel {
     int N = c_tensor->dims()[0];
     int D = c_tensor->dims()[1];
 
-    auto forget_bias = static_cast<T>(ctx.Attr<AttrType>("forget_bias"));
+    auto forget_bias = static_cast<T>(ctx.Attr<float>("forget_bias"));
 
     int block = 512;
     int n = N * D;
@@ -169,5 +169,7 @@ class LstmUnitGradOpCUDAKernel : public framework::OpKernel {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP_GPU_KERNEL(lstm_unit, ops::LstmUnitOpCUDAKernel<float>);
-REGISTER_OP_GPU_KERNEL(lstm_unit_grad, ops::LstmUnitGradOpCUDAKernel<float>);
+REGISTER_OP_GPU_KERNEL(lstm_unit, ops::LstmUnitOpCUDAKernel<float>,
+                       ops::LstmUnitOpCUDAKernel<double>);
+REGISTER_OP_GPU_KERNEL(lstm_unit_grad, ops::LstmUnitGradOpCUDAKernel<float>,
+                       ops::LstmUnitGradOpCUDAKernel<double>);

paddle/operators/lstm_unit_op.h

@@ -32,8 +32,8 @@ inline T tanh(T x) {
   return 2. * sigmoid(2. * x) - 1.;
 }
 
-template <typename Place, typename T, typename AttrType = T>
-class LstmUnitKernel : public framework::OpKernel {
+template <typename Place, typename T>
+class LstmUnitKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()),
@@ -44,7 +44,7 @@ class LstmUnitKernel : public framework::OpKernel {
     auto* c_tensor = ctx.Output<framework::Tensor>("C");
     auto* h_tensor = ctx.Output<framework::Tensor>("H");
 
-    auto forget_bias = static_cast<T>(ctx.Attr<AttrType>("forget_bias"));
+    auto forget_bias = static_cast<T>(ctx.Attr<float>("forget_bias"));
 
     int b_size = c_tensor->dims()[0];
     int D = c_tensor->dims()[1];
@@ -75,8 +75,8 @@ class LstmUnitKernel : public framework::OpKernel {
   }
 };
 
-template <typename Place, typename T, typename AttrType = T>
-class LstmUnitGradKernel : public framework::OpKernel {
+template <typename Place, typename T>
+class LstmUnitGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()),
@@ -108,7 +108,7 @@ class LstmUnitGradKernel : public framework::OpKernel {
     int N = c_tensor->dims()[0];
     int D = c_tensor->dims()[1];
 
-    auto forget_bias = static_cast<T>(ctx.Attr<AttrType>("forget_bias"));
+    auto forget_bias = static_cast<T>(ctx.Attr<float>("forget_bias"));
 
     for (int n = 0; n < N; ++n) {
       for (int d = 0; d < D; ++d) {

paddle/operators/math/CMakeLists.txt

 if(WITH_GPU)
-    nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc
-      im2col.cu pooling.cc pooling.cu DEPS cblas device_context operator)
-    nv_library(softmax_function SRCS softmax.cc softmax.cu
-      DEPS operator)
-    nv_library(cross_entropy_function SRCS cross_entropy.cc cross_entropy.cu
-      DEPS operator)
+    nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc im2col.cu pooling.cc pooling.cu DEPS cblas device_context operator)
+    nv_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)
+    nv_library(softmax SRCS softmax.cc softmax.cu DEPS operator)
+    nv_library(cross_entropy SRCS cross_entropy.cc cross_entropy.cu DEPS operator)
 else()
-    cc_library(math_function SRCS math_function.cc im2col.cc pooling.cc
-      DEPS cblas device_context operator)
-    cc_library(softmax_function SRCS softmax.cc DEPS operator)
-    cc_library(cross_entropy_function SRCS cross_entropy.cc DEPS operator)
+    cc_library(math_function SRCS math_function.cc im2col.cc pooling.cc DEPS cblas device_context operator)
+    cc_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)
+    cc_library(softmax SRCS softmax.cc DEPS operator)
+    cc_library(cross_entropy SRCS cross_entropy.cc DEPS operator)
 endif()
 
-nv_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)
 cc_test(im2col_test SRCS im2col_test.cc DEPS math_function tensor)

paddle/operators/math/cross_entropy.cc

@@ -26,8 +26,8 @@ using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
 template <typename T>
 class CrossEntropyFunctor<platform::CPUPlace, T> {
  public:
-  void operator()(const framework::ExecutionContext& ctx,
-                  framework::Tensor* out, const framework::Tensor* prob,
+  void operator()(const platform::DeviceContext& ctx, framework::Tensor* out,
+                  const framework::Tensor* prob,
                   const framework::Tensor* labels, const bool softLabel) {
     const int batch_size = prob->dims()[0];
     if (softLabel) {
@@ -35,7 +35,7 @@ class CrossEntropyFunctor<platform::CPUPlace, T> {
       auto lbl = EigenMatrix<T>::From(*labels);
       auto loss = EigenMatrix<T>::From(*out);
 
-      loss.device(ctx.GetEigenDevice<platform::CPUPlace>()) =
+      loss.device(*ctx.GetEigenDevice<platform::CPUPlace>()) =
           -((lbl * in.log().unaryExpr(math::TolerableValue<T>()))
                 .sum(Eigen::DSizes<int, 1>(1))
                 .reshape(Eigen::DSizes<int, 2>(batch_size, 1)));

paddle/operators/math/cross_entropy.cu

@@ -74,8 +74,8 @@ using Tensor = framework::Tensor;
 template <typename T>
 class CrossEntropyFunctor<platform::GPUPlace, T> {
  public:
-  void operator()(const framework::ExecutionContext& ctx,
-                  framework::Tensor* out, const framework::Tensor* prob,
+  void operator()(const platform::DeviceContext& ctx, framework::Tensor* out,
+                  const framework::Tensor* prob,
                   const framework::Tensor* labels, bool softLabel) {
     const T* prob_data = prob->data<T>();
     T* loss_data = out->mutable_data<T>(ctx.GetPlace());
@@ -87,20 +87,18 @@ class CrossEntropyFunctor<platform::GPUPlace, T> {
       const T* label_data = labels->data<T>();
       int block = class_num > 512 ? 512 : pow(2, int(std::log2(class_num)));
 
-      SoftCrossEntropyKernel<
-          T><<<batch_size, block, block * sizeof(T),
-               reinterpret_cast<const platform::CUDADeviceContext&>(
-                   ctx.device_context())
-                   .stream()>>>(loss_data, prob_data, label_data, class_num);
+      SoftCrossEntropyKernel<T><<<
+          batch_size, block, block * sizeof(T),
+          reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream()>>>(
+          loss_data, prob_data, label_data, class_num);
     } else {
       const int* label_data = labels->data<int>();
       int block = 512;
       int grid = (batch_size + block - 1) / block;
       CrossEntropyKernel<T><<<
-          grid, block, 0, reinterpret_cast<const platform::CUDADeviceContext&>(
-                              ctx.device_context())
-                              .stream()>>>(loss_data, prob_data, label_data,
-                                           batch_size, class_num);
+          grid, block, 0,
+          reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream()>>>(
+          loss_data, prob_data, label_data, batch_size, class_num);
     }
   }
 };

paddle/operators/math/cross_entropy.h

@@ -37,9 +37,7 @@ struct TolerableValue {
 template <typename Place, typename T>
 class CrossEntropyFunctor {
  public:
-  // (TODO caoying) it is much better to use DeviceContext as the first
-  // parameter.
-  void operator()(const framework::ExecutionContext& context,
+  void operator()(const platform::DeviceContext& context,
                   framework::Tensor* out, const framework::Tensor* prob,
                   const framework::Tensor* labels, const bool softLabel);
 };

paddle/operators/math/math_function.h

@@ -52,6 +52,7 @@ int LAPACKE_dgetri(int matrix_layout, int n, double* a, int lda,
 
 #include <cmath>
 
+#include "paddle/framework/eigen.h"
 #include "paddle/framework/tensor.h"
 #include "paddle/platform/device_context.h"
 #include "paddle/platform/enforce.h"
@@ -84,6 +85,13 @@ void matmul(const platform::DeviceContext& context,
             const framework::Tensor& matrix_b, bool trans_b, T alpha,
             framework::Tensor* matrix_out, T beta);
 
+template <typename Place, typename T>
+void SetConstant(const platform::DeviceContext& context,
+                 framework::Tensor* tensor, T num) {
+  auto t = framework::EigenVector<T>::Flatten(*tensor);
+  t.device(*context.GetEigenDevice<Place>()) = t.constant(static_cast<T>(num));
+}
+
 }  // namespace math
 }  // namespace operators
 }  // namespace paddle

paddle/operators/math/math_function_test.cc

@@ -243,3 +243,24 @@ TEST(math_function, gemm_trans_clbas) {
   EXPECT_EQ(input3_ptr[6], 86);
   EXPECT_EQ(input3_ptr[7], 99);
 }
+
+TEST(math_function, zero) {
+  paddle::framework::Tensor tensor;
+  auto* cpu_place = new paddle::platform::CPUPlace();
+  float* t = tensor.mutable_data<float>({2, 2}, *cpu_place);
+  paddle::platform::CPUDeviceContext context(*cpu_place);
+  paddle::operators::math::SetConstant<paddle::platform::CPUPlace, float>(
+      context, &tensor, 0);
+  EXPECT_EQ(t[0], 0);
+  EXPECT_EQ(t[1], 0);
+  EXPECT_EQ(t[2], 0);
+  EXPECT_EQ(t[3], 0);
+
+  paddle::operators::math::SetConstant<paddle::platform::CPUPlace, float>(
+      context, &tensor, 1);
+
+  EXPECT_EQ(t[0], 1);
+  EXPECT_EQ(t[1], 1);
+  EXPECT_EQ(t[2], 1);
+  EXPECT_EQ(t[3], 1);
+}

paddle/operators/math/pooling.h

@@ -22,43 +22,98 @@ namespace paddle {
 namespace operators {
 namespace math {
 //////////////////////
-#define FLT_MAX __FLT_MAX__
+#define FLT_MAX __FLT_MAX__  //
 
-template <typename Place, typename T>
-class MaxPool2dWithIndexFunctor {
+template <class T>
+class MaxPool {
+ public:
+  DEVICE inline T initial() { return static_cast<T>(-FLT_MAX); }
+  DEVICE inline void compute(T& y, const T& x) { y = y > x ? y : x; }
+  DEVICE inline void finalize(T& y, const T& poo_size) {}
+};
+
+template <class T>
+class AvgPool {
+ public:
+  DEVICE inline T initial() { return static_cast<T>(0); }
+  DEVICE inline void compute(T& y, const T& x) { y += x; }
+  DEVICE inline void finalize(T& y, const T& poo_size) { y /= poo_size; }
+};
+template <class T>
+class MaxPoolGrad {
+ public:
+  DEVICE inline void compute(const T& x, const T& y, const T& dy, T& dx,
+                             T scale) {
+    dx += dy * (x == y);
+  }
+};
+
+template <class T>
+class AvgPoolGrad {
+ public:
+  DEVICE inline void compute(const T& x, const T& y, const T& dy, T& dx,
+                             T scale) {
+    dx += (scale * dy);
+  }
+};
+
+template <typename Place, typename PoolProcess, typename T>
+class Pool2dFunctor {
  public:
   void operator()(const platform::DeviceContext& context,
                   const framework::Tensor& input, framework::Tensor& output,
-                  framework::Tensor& mask, std::vector<int>& ksize,
-                  std::vector<int>& strides, std::vector<int>& paddings);
+                  std::vector<int>& ksize, std::vector<int>& strides,
+                  std::vector<int>& paddings, PoolProcess pool_compute);
+};
+
+template <typename Place, typename PoolProcess, typename T>
+class Pool2dGradFunctor {
+ public:
+  void operator()(const platform::DeviceContext& context,
+                  const framework::Tensor& input, framework::Tensor& input_grad,
+                  const framework::Tensor& output,
+                  const framework::Tensor& output_grad, std::vector<int>& ksize,
+                  std::vector<int>& strides, std::vector<int>& paddings,
+                  PoolProcess pool_compute);
 };
 
-template <typename Place, typename T>
-class MaxPool2dWithIndexGradFunctor {
+template <typename Place, class T>
+class MaxPool2dGradFunctor {
  public:
   void operator()(const platform::DeviceContext& context,
-                  framework::Tensor& input_grad,
-                  const framework::Tensor& output_grad,
-                  const framework::Tensor& mask, std::vector<int>& ksize,
+                  const framework::Tensor& input, framework::Tensor& input_grad,
+                  const framework::Tensor& output,
+                  const framework::Tensor& output_grad, std::vector<int>& ksize,
                   std::vector<int>& strides, std::vector<int>& paddings);
 };
 
-template <typename Place, typename T>
-class MaxPool3dWithIndexFunctor {
+template <typename Place, typename PoolProcess, typename T>
+class Pool3dFunctor {
  public:
   void operator()(const platform::DeviceContext& context,
                   const framework::Tensor& input, framework::Tensor& output,
-                  framework::Tensor& mask, std::vector<int>& ksize,
-                  std::vector<int>& strides, std::vector<int>& paddings);
+                  std::vector<int>& ksize, std::vector<int>& strides,
+                  std::vector<int>& paddings, PoolProcess pool_compute);
+};
+
+template <typename Place, typename PoolProcess, typename T>
+class Pool3dGradFunctor {
+ public:
+  void operator()(const platform::DeviceContext& context,
+                  const framework::Tensor& input, framework::Tensor& input_grad,
+                  const framework::Tensor& output,
+                  const framework::Tensor& output_grad, std::vector<int>& ksize,
+                  std::vector<int>& strides, std::vector<int>& paddings,
+                  PoolProcess pool_compute);
 };
 
-template <typename Place, typename T>
-class MaxPool3dWithIndexGradFunctor {
+template <typename Place, class T>
+class MaxPool3dGradFunctor {
  public:
   void operator()(const platform::DeviceContext& context,
-                  framework::Tensor& input_grad,
-                  const framework::Tensor& output_grad,
-                  const framework::Tensor& mask, std::vector<int>& ksize,
+                  const framework::Tensor& input, framework::Tensor& input_grad,
+                  const framework::Tensor& output,
+                  const framework::Tensor& output_grad, std::vector<int>& ksize,
                   std::vector<int>& strides, std::vector<int>& paddings);
 };
 

paddle/operators/math/softmax.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
+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
+    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. */
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
 
 #include "paddle/operators/math/softmax.h"
 
@@ -19,6 +19,7 @@ namespace operators {
 namespace math {
 
 template class SoftmaxFunctor<platform::CPUPlace, float>;
+template class SoftmaxGradFunctor<platform::CPUPlace, float>;
 
 }  // namespace math
 }  // namespace operators

paddle/operators/math/softmax.cu

 /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
 
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
+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
+    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. */
+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. */
 
 #define EIGEN_USE_GPU
 
@@ -21,6 +21,7 @@ namespace operators {
 namespace math {
 
 template class SoftmaxFunctor<platform::GPUPlace, float>;
+template class SoftmaxGradFunctor<platform::GPUPlace, float>;
 
 }  // namespace math
 }  // namespace operators

paddle/operators/math/softmax.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
+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
+    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. */
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
 
 #pragma once
 #include "paddle/framework/eigen.h"
@@ -36,7 +36,7 @@ struct ValueClip {
 template <typename Place, typename T>
 class SoftmaxFunctor {
  public:
-  void operator()(const framework::ExecutionContext& context,
+  void operator()(const platform::DeviceContext& context,
                   const framework::Tensor* X, framework::Tensor* Y) {
     auto logits = EigenMatrix<T>::From(*X);
     auto softmax = EigenMatrix<T>::From(*Y);
@@ -58,8 +58,8 @@ class SoftmaxFunctor {
                                .broadcast(one_by_class))
                               .unaryExpr(ValueClip<T>());
 
-    softmax.device(context.GetEigenDevice<Place>()) = shifted_logits.exp();
-    softmax.device(context.GetEigenDevice<Place>()) =
+    softmax.device(*context.GetEigenDevice<Place>()) = shifted_logits.exp();
+    softmax.device(*context.GetEigenDevice<Place>()) =
         (softmax *
          softmax.sum(along_class)
              .inverse()
@@ -68,6 +68,37 @@ class SoftmaxFunctor {
              .broadcast(one_by_class));
   }
 };
+
+template <typename Place, typename T>
+class SoftmaxGradFunctor {
+ public:
+  void operator()(const platform::DeviceContext& context,
+                  const framework::Tensor* y, const framework::Tensor* y_grad,
+                  framework::Tensor* x_grad) {
+    auto softmax = EigenMatrix<T>::From(*y);
+    auto softmax_grad = EigenMatrix<T>::From(*y_grad);
+    auto logits_grad = EigenMatrix<T>::From(*x_grad);
+
+    const int kBatchDim = 0;
+    const int kClassDim = 1;
+
+    const int batch_size = softmax.dimension(kBatchDim);
+    const int num_classes = softmax.dimension(kClassDim);
+
+    Eigen::DSizes<int, 1> along_class(kClassDim);
+    Eigen::DSizes<int, 2> batch_by_one(batch_size, 1);
+    Eigen::DSizes<int, 2> one_by_class(1, num_classes);
+
+    auto dot = (softmax * softmax_grad)
+                   .sum(along_class)
+                   .eval()
+                   .reshape(batch_by_one)
+                   .broadcast(one_by_class);
+    logits_grad.device(*context.GetEigenDevice<Place>()) =
+        (softmax_grad - dot) * softmax;
+  }
+};
+
 }  // namespace math
 }  // namespace operators
 }  // namespace paddle

paddle/operators/mean_op.h

@@ -28,7 +28,7 @@ template <typename T, int MajorType = Eigen::RowMajor,
 using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
 
 template <typename Place, typename T>
-class MeanKernel : public framework::OpKernel {
+class MeanKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto* input = context.Input<Tensor>("X");
@@ -45,7 +45,7 @@ class MeanKernel : public framework::OpKernel {
 };
 
 template <typename Place, typename T>
-class MeanGradKernel : public framework::OpKernel {
+class MeanGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto OG = context.Input<Tensor>(framework::GradVarName("Out"));

paddle/operators/minus_op.h

@@ -20,7 +20,7 @@ namespace paddle {
 namespace operators {
 
 template <typename Place, typename T>
-class MinusKernel : public framework::OpKernel {
+class MinusKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto* left_tensor = context.Input<framework::Tensor>("X");

paddle/operators/modified_huber_loss_op.cu

@@ -39,7 +39,7 @@ struct ModifiedHuberLossBackward {
 };
 
 template <typename T>
-class ModifiedHuberLossGradGPUKernel : public framework::OpKernel {
+class ModifiedHuberLossGradGPUKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto* in0 = context.Input<Tensor>("Y");

paddle/operators/modified_huber_loss_op.h

@@ -47,7 +47,7 @@ struct ModifiedHuberLossForward {
 };
 
 template <typename Place, typename T>
-class ModifiedHuberLossKernel : public framework::OpKernel {
+class ModifiedHuberLossKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto* in0 = context.Input<Tensor>("X");
@@ -73,7 +73,7 @@ class ModifiedHuberLossKernel : public framework::OpKernel {
 
 // CPU backward kernel
 template <typename T>
-class ModifiedHuberLossGradCPUKernel : public framework::OpKernel {
+class ModifiedHuberLossGradCPUKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto* in0 = context.Input<Tensor>("Y");

paddle/operators/mul_op.cc

 /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
 
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
+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
+    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. */
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
 
 #include "paddle/operators/mul_op.h"
 
@@ -35,12 +35,14 @@ class MulOp : public framework::OperatorWithKernel {
     int x_num_col_dims = ctx->Attrs().Get<int>("x_num_col_dims");
     int y_num_col_dims = ctx->Attrs().Get<int>("y_num_col_dims");
 
-    PADDLE_ENFORCE(x_dims.size() > x_num_col_dims,
-                   "The rank of input tensor X should be larger than "
-                   "`mul_op`'s `x_num_col_dims`.");
-    PADDLE_ENFORCE(y_dims.size() > y_num_col_dims,
-                   "The rank of input tensor Y should be larger than "
-                   "`mul_op`'s `y_num_col_dims`.");
+    PADDLE_ENFORCE_GT(
+        x_dims.size(), x_num_col_dims,
+        "The input tensor X's rank of MulOp should be larger than "
+        "x_num_col_dims.");
+    PADDLE_ENFORCE_GT(
+        y_dims.size(), y_num_col_dims,
+        "The input tensor Y's rank of MulOp should be larger than "
+        "y_num_col_dims.");
 
     auto x_mat_dims = framework::flatten_to_2d(x_dims, x_num_col_dims);
     auto y_mat_dims = framework::flatten_to_2d(y_dims, y_num_col_dims);

paddle/operators/mul_op.h

@@ -28,7 +28,7 @@ template <typename T, int MajorType = Eigen::RowMajor,
 using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
 
 template <typename Place, typename T>
-class MulKernel : public framework::OpKernel {
+class MulKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     const Tensor* x = context.Input<Tensor>("X");
@@ -52,7 +52,7 @@ class MulKernel : public framework::OpKernel {
 };
 
 template <typename Place, typename T>
-class MulGradKernel : public framework::OpKernel {
+class MulGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     int x_num_col_dims = ctx.template Attr<int>("x_num_col_dims");

paddle/operators/multiplex_op.cc

@@ -50,6 +50,11 @@ class MultiplexOp : public framework::OperatorWithKernel {
     }
     ctx->SetOutputDim("Out", in_dim);
   }
+
+  framework::DataType IndicateDataType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::ToDataType(ctx.MultiInput<Tensor>("X")[0]->type());
+  }
 };
 
 class MultiplexOpMaker : public framework::OpProtoAndCheckerMaker {
@@ -99,6 +104,11 @@ class MultiplexGradOp : public framework::OperatorWithKernel {
     }
     ctx->SetOutputsDim(framework::GradVarName("X"), d_ins);
   }
+
+  framework::DataType IndicateDataType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::ToDataType(ctx.MultiInput<Tensor>("X")[0]->type());
+  }
 };
 
 }  // namespace operators

paddle/operators/multiplex_op.cu

@@ -21,7 +21,7 @@ namespace operators {
 using Tensor = framework::Tensor;
 
 template <typename Place, typename T>
-class MultiplexGPUKernel : public framework::OpKernel {
+class MultiplexGPUKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const {
     auto ins = ctx.MultiInput<Tensor>("X");
@@ -51,7 +51,7 @@ class MultiplexGPUKernel : public framework::OpKernel {
 };
 
 template <typename Place, typename T>
-class MultiplexGradGPUKernel : public framework::OpKernel {
+class MultiplexGradGPUKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const {
     auto* d_out = ctx.Input<Tensor>(framework::GradVarName("Out"));

paddle/operators/multiplex_op.h

@@ -23,7 +23,7 @@ namespace paddle {
 namespace operators {
 
 template <typename Place, typename T>
-class MultiplexCPUKernel : public framework::OpKernel {
+class MultiplexCPUKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const {
     auto ins = ctx.MultiInput<framework::Tensor>("X");
@@ -48,7 +48,7 @@ class MultiplexCPUKernel : public framework::OpKernel {
 };
 
 template <typename Place, typename T>
-class MultiplexGradCPUKernel : public framework::OpKernel {
+class MultiplexGradCPUKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const {
     auto* d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));

paddle/operators/pad_op.h

@@ -47,7 +47,7 @@ void PadFunction(const framework::ExecutionContext& context) {
 }
 
 template <typename Place, typename T>
-class PadKernel : public framework::OpKernel {
+class PadKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     int rank = context.Input<Tensor>("X")->dims().size();
@@ -97,7 +97,7 @@ void PadGradFunction(const framework::ExecutionContext& context) {
 }
 
 template <typename Place, typename T>
-class PadGradKernel : public framework::OpKernel {
+class PadGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     size_t rank =

paddle/operators/pool_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/operators/pool_op.h"
+
+namespace paddle {
+namespace operators {
+
+int OutputSizePool(int input_size, int filter_size, int padding, int stride) {
+  int output_size = (input_size - filter_size + 2 * padding) / stride + 1;
+  return output_size;
+}
+
+class PoolOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContextBase *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "X(Input) of Pooling should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Out(Output) of Pooling should not be null.");
+
+    auto in_x_dims = ctx->GetInputDim("X");
+
+    std::string pooling_type = ctx->Attrs().Get<std::string>("poolingType");
+    std::vector<int> ksize = ctx->Attrs().Get<std::vector<int>>("ksize");
+    std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
+    std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
+
+    PADDLE_ENFORCE(pooling_type == "max" || pooling_type == "avg",
+                   "pooling_type should be 'max' or 'avg'");
+    PADDLE_ENFORCE(in_x_dims.size() == 4 || in_x_dims.size() == 5,
+                   "Pooling intput should be 4-D or 5-D");
+
+    if (ctx->Attrs().Get<bool>("globalPooling")) {
+      ksize.resize(static_cast<size_t>(in_x_dims.size()) - 2);
+      for (size_t i = 0; i < ksize.size(); ++i)
+        ksize[i] = static_cast<int>(in_x_dims[i + 2]);
+    }
+
+    PADDLE_ENFORCE(in_x_dims.size() - ksize.size() == 2U,
+                   "Input size and Pooling size should be consistent.");
+    PADDLE_ENFORCE(ksize.size() == 2 || ksize.size() == 3,
+                   "Pooling size should be 2 elements. or 3 elements.");
+    PADDLE_ENFORCE_EQ(ksize.size(), strides.size(),
+                      "strides size and pooling size should be the same.");
+    PADDLE_ENFORCE_EQ(ksize.size(), paddings.size(),
+                      "paddings size and pooling size should be the same.");
+
+    std::vector<int64_t> output_shape({in_x_dims[0], in_x_dims[1]});
+    for (size_t i = 0; i < ksize.size(); ++i) {
+      output_shape.push_back(
+          OutputSizePool(in_x_dims[i + 2], ksize[i], paddings[i], strides[i]));
+    }
+    ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
+  }
+};
+
+class PoolOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContextBase *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "X(Input) of Pooling should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
+                   "Input@Grad of Pooling should not be null.");
+    ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
+  }
+};
+
+class Pool2dOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  Pool2dOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput(
+        "X",
+        "The input tensor of pooling operator. "
+        "The format of input tensor is NCHW. Where N is batch size, C is the "
+        "number of channels, H and W is the height and width of feature.");
+    AddOutput("Out",
+              "The output tensor of pooling operator."
+              "The format of output tensor is also NCHW.");
+
+    AddAttr<std::string>("poolingType",
+                         "PoolingType of pooling operator."
+                         "Str constant equal to 'max' or 'avg'.")
+        .InEnum({"max", "avg"});
+    AddAttr<std::vector<int>>(
+        "ksize",
+        "Pooling size(depth, height, width) of pooling operator."
+        "If globalPooling = true, ksize is ignored and need not be "
+        "specified.");  // TODO(Add checker)
+    AddAttr<bool>(
+        "globalPooling",
+        "Whether to use the globalPooling."
+        "Bool constant equal to false or true."
+        "Default false."
+        "If globalPooling = true, ksize is ignored and need not be specified.")
+        .SetDefault(false);
+    AddAttr<std::vector<int>>("strides",
+                              "Strides(height, width) of pooling operator."
+                              "Default {1,1}")
+        .SetDefault({1, 1});  // TODO(Add checker)
+    AddAttr<std::vector<int>>("paddings",
+                              "Paddings(height, width) of pooling operator."
+                              "Default {0,0}.")
+        .SetDefault({0, 0});  // TODO(Add checker)
+    AddComment(R"DOC(
+The pooling2d operation calculates the output based on
+the input, poolingType and ksize, strides, paddings parameters.
+)DOC");
+  }
+};
+
+class Pool3dOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  Pool3dOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X",
+             "The input tensor of pooling operator. "
+             "The format of input tensor is NCDHW. Where N is batch size, C is "
+             "the "
+             "number of channels, D, H and W is the depth, height and width of "
+             "feature.");
+    AddOutput("Out",
+              "The output tensor of pooling operator."
+              "The format of output tensor is also NCDHW.");
+
+    AddAttr<std::string>("poolingType",
+                         "PoolingType of pooling operator."
+                         "str constant equal to 'max' or 'avg'.")
+        .InEnum({"max", "avg"});
+    AddAttr<std::vector<int>>(
+        "ksize",
+        "Pooling size(depth, height, width) of pooling operator."
+        "If globalPooling = true, ksize is ignored and need not be "
+        "specified.");  // TODO(Add checker)
+    AddAttr<bool>(
+        "globalPooling",
+        "Whether to use the globalPooling."
+        "Bool constant equal to false or true."
+        "Default false."
+        "If globalPooling = true, ksize is ignored and need not be specified.")
+        .SetDefault(false);
+    AddAttr<std::vector<int>>(
+        "strides",
+        "Strides(depth, height, width) of pooling operator."
+        "Default {1,1,1}.")
+        .SetDefault({1, 1, 1});  // TODO(Add checker)
+    AddAttr<std::vector<int>>(
+        "paddings",
+        "Paddings(depth, height, width) of pooling operator."
+        "Default {0,0,0}.")
+        .SetDefault({0, 0, 0});  // TODO(Add checker)
+    AddComment(R"DOC(
+The pooling3d operation calculates the output based on
+the input, poolingType and ksize, strides, paddings parameters.
+)DOC");
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP(pool2d, ops::PoolOp, ops::Pool2dOpMaker, pool2d_grad,
+            ops::PoolOpGrad);
+
+REGISTER_OP_CPU_KERNEL(pool2d,
+                       ops::PoolKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(pool2d_grad,
+                       ops::PoolGradKernel<paddle::platform::CPUPlace, float>)
+
+REGISTER_OP(pool3d, ops::PoolOp, ops::Pool3dOpMaker, pool3d_grad,
+            ops::PoolOpGrad);
+
+REGISTER_OP_CPU_KERNEL(pool3d,
+                       ops::PoolKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(pool3d_grad,
+                       ops::PoolGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/pool_op.cu

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/operators/pool_op.h"
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_GPU_KERNEL(pool2d,
+                       ops::PoolKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(pool2d_grad,
+                       ops::PoolGradKernel<paddle::platform::GPUPlace, float>);
+
+REGISTER_OP_GPU_KERNEL(pool3d,
+                       ops::PoolKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(pool3d_grad,
+                       ops::PoolGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/pool_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/operators/math/math_function.h"
+#include "paddle/operators/math/pooling.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename Place, typename T>
+class PoolKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    const Tensor* in_x = context.Input<Tensor>("X");
+    Tensor* out = context.Output<Tensor>("Out");
+
+    std::string pooling_type = context.Attr<std::string>("poolingType");
+    std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
+    std::vector<int> strides = context.Attr<std::vector<int>>("strides");
+    std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
+    if (context.Attr<bool>("globalPooling")) {
+      for (size_t i = 0; i < ksize.size(); ++i) {
+        ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
+      }
+    }
+
+    switch (ksize.size()) {
+      case 2: {
+        if (pooling_type == "max") {
+          paddle::operators::math::Pool2dFunctor<
+              Place, paddle::operators::math::MaxPool<T>, T>
+              pool2d_forward;
+          paddle::operators::math::MaxPool<T> pool_process;
+          pool2d_forward(context.device_context(), *in_x, *out, ksize, strides,
+                         paddings, pool_process);
+
+        } else if (pooling_type == "avg") {
+          paddle::operators::math::Pool2dFunctor<
+              Place, paddle::operators::math::AvgPool<T>, T>
+              pool2d_forward;
+          paddle::operators::math::AvgPool<T> pool_process;
+          pool2d_forward(context.device_context(), *in_x, *out, ksize, strides,
+                         paddings, pool_process);
+        }
+      } break;
+      case 3: {
+        if (pooling_type == "max") {
+          paddle::operators::math::Pool3dFunctor<
+              Place, paddle::operators::math::MaxPool<T>, T>
+              pool3d_forward;
+          paddle::operators::math::MaxPool<T> pool_process;
+          pool3d_forward(context.device_context(), *in_x, *out, ksize, strides,
+                         paddings, pool_process);
+        } else if (pooling_type == "avg") {
+          paddle::operators::math::Pool3dFunctor<
+              Place, paddle::operators::math::AvgPool<T>, T>
+              pool3d_forward;
+          paddle::operators::math::AvgPool<T> pool_process;
+          pool3d_forward(context.device_context(), *in_x, *out, ksize, strides,
+                         paddings, pool_process);
+        }
+      } break;
+    }
+  }
+};
+
+template <typename Place, typename T>
+class PoolGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    const Tensor* in_x = context.Input<Tensor>("X");
+    const Tensor* out = context.Input<Tensor>("Out");
+    const Tensor* out_grad =
+        context.Input<Tensor>(framework::GradVarName("Out"));
+    Tensor* in_x_grad = context.Output<Tensor>(framework::GradVarName("X"));
+
+    std::string pooling_type = context.Attr<std::string>("poolingType");
+    std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
+    std::vector<int> strides = context.Attr<std::vector<int>>("strides");
+    std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
+
+    if (context.Attr<bool>("globalPooling")) {
+      for (size_t i = 0; i < ksize.size(); ++i)
+        ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
+    }
+
+    if (in_x_grad) {
+      in_x_grad->mutable_data<T>(context.GetPlace());
+      auto temp = framework::EigenVector<T>::Flatten(*in_x_grad);
+      temp.device(context.GetEigenDevice<Place>()) =
+          temp.constant(static_cast<T>(0));
+
+      switch (ksize.size()) {
+        case 2: {
+          if (pooling_type == "max") {
+            paddle::operators::math::MaxPool2dGradFunctor<Place, T>
+                pool2d_backward;
+            pool2d_backward(context.device_context(), *in_x, *in_x_grad, *out,
+                            *out_grad, ksize, strides, paddings);
+          } else if (pooling_type == "avg") {
+            paddle::operators::math::Pool2dGradFunctor<
+                Place, paddle::operators::math::AvgPoolGrad<T>, T>
+                pool2d_backward;
+            paddle::operators::math::AvgPoolGrad<T> pool_process;
+            pool2d_backward(context.device_context(), *in_x, *in_x_grad, *out,
+                            *out_grad, ksize, strides, paddings, pool_process);
+          }
+        } break;
+        case 3: {
+          if (pooling_type == "max") {
+            paddle::operators::math::MaxPool3dGradFunctor<Place, T>
+                pool3d_backward;
+            pool3d_backward(context.device_context(), *in_x, *in_x_grad, *out,
+                            *out_grad, ksize, strides, paddings);
+          } else if (pooling_type == "avg") {
+            paddle::operators::math::Pool3dGradFunctor<
+                Place, paddle::operators::math::AvgPoolGrad<T>, T>
+                pool3d_backward;
+            paddle::operators::math::AvgPoolGrad<T> pool_process;
+            pool3d_backward(context.device_context(), *in_x, *in_x_grad, *out,
+                            *out_grad, ksize, strides, paddings, pool_process);
+          }
+        } break;
+      }
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/operators/scale_op.h

@@ -20,7 +20,7 @@
 namespace paddle {
 namespace operators {
 template <typename Place, typename T, typename AttrType = T>
-class ScaleKernel : public framework::OpKernel {
+class ScaleKernel : public framework::OpKernel<T> {
  public:
   virtual void Compute(const framework::ExecutionContext& context) const {
     auto* tensor = context.Output<framework::Tensor>("Out");