diff --git a/develop/doc/_sources/design/refactorization.md.txt b/develop/doc/_sources/design/refactorization.md.txt index 629422e7743af666b42fd69fbff442ce15bef596..ec51aa1a0ec667175ff7215dcd359023e296769f 100644 --- a/develop/doc/_sources/design/refactorization.md.txt +++ b/develop/doc/_sources/design/refactorization.md.txt @@ -17,22 +17,22 @@ The goals of refactoring include: 1. A graph is composed of *variables* and *operators*. -1. The description of graphs must be capable of being serialized/deserialized, so that: +1. The description of graphs must be serializable/deserializable, so that: - 1. It can to be sent to the cloud for distributed execution, and + 1. It can 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 does the following steps +1. The Python program does two things - 1. *compilation*: run a Python program to generate a protobuf message representation of the graph and send it to + 1. *Compilation* runs 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*: 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. + 1. *Execution* executes 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 of Computation Graph -At compile time, the Python program generates a 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 a description of the graph. At runtime, the C++ program realizes the graph and runs it. @@ -42,11 +42,11 @@ At runtime, the C++ program realizes the graph and runs 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 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 `}`). +The word *graph* is interchangeable with *block* in this document. A graph consists of computation steps and local variables similar to a C++/Java program block, or a pair of parentheses(`{` and `}`). ## Compilation and Execution -1. Run an application Python program to describe the graph. In particular, the Python application program does the following: +1. Run a 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, @@ -54,10 +54,10 @@ The word *graph* is interchangeable with *block* in this document. A graph repr 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. Add optimization operators to the computation graph. + 1. Optionally, split the graph for distributed training. -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. The invocation of `train` or [`infer`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/inference.py#L108) methods in the 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. realize local variables defined in the BlockDesc message in the new scope, @@ -107,8 +107,8 @@ Compile Time -> IR -> Runtime ![class_diagram](http://api.paddlepaddle.org/graphviz?dot=https://gist.githubusercontent.com/reyoung/53df507f6749762675dff3e7ce53372f/raw/dd598e8f1976f5759f58af5e5ef94738a6b2e661/op.dot) * `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. + * Operator stores input/output variable names and attributes. + * The `InferShape` interface is used to infer the shape of the output variables based on the shapes of the input variables. * Use `Run` to compute the `output` variables from the `input` variables. --- @@ -139,7 +139,7 @@ Compile Time -> IR -> Runtime * 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`. + * `thrust`, in addition, supports more complex APIs, like `scan`, `reduce`, `reduce_by_key`. * Hand-writing `GPUKernel` and `CPU` 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.) --- @@ -185,10 +185,10 @@ Make sure the registration process is executed and linked. 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` + 1. Call maker class to complete `proto` and `checker` 2. Using the completed `proto` and `checker`, it will add a new key-value pair to the `OpInfoMap` -4. Invoke the `USE` macro in which the Op is used, to make sure that it is linked. +4. Invoke the `USE` macro in which the Op is used to make sure that it is linked. --- # Backward Module (1/2) @@ -199,13 +199,14 @@ Make sure the registration process is executed and linked. --- # Backward Module (2/2) ### Build Backward Network -- **Input**: graph of forward operators -- **Output**: graph of backward operators +- **Input**: a graph of forward operators +- **Output**: a 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 + - RNN Op => recursively call `Backward` on stepnet --- @@ -215,10 +216,10 @@ Make sure the registration process is executed and linked. * Only dims and data pointers are stored in `Tensor`. * 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`. +* `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 +* `Scope` is where variables are stored. + * map * `Scope` has a hierarchical structure. The local scope can get variables from its parent scope. --- @@ -246,7 +247,7 @@ Make sure the registration process is executed and linked. --- # Control the migration quality - Compare the performance of migrated models with old ones. -- Follow the google C++ style +- Follow the google C++ style guide. - 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. diff --git a/develop/doc/design/refactorization.html b/develop/doc/design/refactorization.html index 671c0ace3d889e1457792a925fd925c520851c03..c280706efc9613e3ed678e29a54c110f1bf0e1e7 100644 --- a/develop/doc/design/refactorization.html +++ b/develop/doc/design/refactorization.html @@ -193,49 +193,49 @@
  • Please refer to computation graphs for a concrete example.
  • Users write Python programs to describe the graphs and run them (locally or remotely).
  • A graph is composed of variables and operators.
    • -
  • The description of graphs must be capable of being serialized/deserialized, so that:
      -
    1. It can to be sent to the cloud for distributed execution, and
      2. +
    2. The description of graphs must be serializable/deserializable, so that:
        +
      1. It can be sent to the cloud for distributed execution, and
      2. It can be sent to clients for mobile or enterprise deployment.
      3. -
    3. The Python program does the following steps
        -
      1. compilation: run a Python program to generate a protobuf message representation of the graph and send it to
          +
        1. The Python program does two things
            +
          1. Compilation runs 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,
            2. the master process of a distributed training job for training, or
            3. the server process of a Kubernetes serving job for distributed serving.
            2. -
          2. execution: execute the graph by constructing instances of class Variable and OperatorBase, according to the protobuf message.
            3. +
          3. Execution executes the graph by constructing instances of class Variable and OperatorBase, according to the protobuf message.

        Description and Realization of Computation Graph

        -

        At compile time, the Python program generates a 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 a description of the graph.

        At runtime, the C++ program realizes the graph and runs it.

        | | Representation (protobuf messages) | Realization (C++ class objects) | |—|—|—| |Data|VarDesc|Variable| |Operation|OpDesc|Operator| |Block|BlockDesc|Block|

        -

        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 }).

        +

        The word graph is interchangeable with block in this document. A graph consists of computation steps and local variables similar to a C++/Java program block, or a pair of parentheses({ and }).

        Compilation and Execution

          -
        1. Run an application Python program to describe the graph. In particular, the Python application program does the following:
            +
          1. Run a Python program to describe the graph. In particular, the Python application program does the following:
            1. Create VarDesc to represent local/intermediate variables,
            2. Create operators and set attributes,
            3. Validate attribute values,
            4. Infer the type and the shape of variables,
            5. Plan memory-reuse for variables,
            6. Generate the backward graph
              7. -
            7. Optimize the computation graph.
              8. -
            8. Potentially, split the graph for distributed training.
              9. +
            9. Add optimization operators to the computation graph.
              10. +
            10. Optionally, split the graph for distributed training.
            2. -
          2. The invocation of train or infer methods in the application Python program does the following:
              +
            1. The invocation of train or infer methods in the Python program does the following:
              1. Create a new Scope instance in the scope hierarchy for each run of a block,
                1. realize local variables defined in the BlockDesc message in the new scope,
                2. a scope is similar to the stack frame in programming languages,
                  3. @@ -298,8 +298,8 @@

                  class_diagram

                  • 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.
                      • +
                    • Operator stores input/output variable names and attributes.
                      • +
                    • The InferShape interface is used to infer the shape of the output variables based on the shapes of the input variables.
                    • Use Run to compute the output variables from the input variables.
                    • @@ -343,7 +343,7 @@
                  • 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.
                      • +
                    • thrust, in addition, supports more complex APIs, like scan, reduce, reduce_by_key.
                  • Hand-writing GPUKernel and CPU code
                      @@ -405,11 +405,11 @@
                    • Write an Op class and its gradient Op class, if required.
                    • Write an Op maker class. In the constructor of this class, describe the inputs, outputs and attributes of the operator.
                    • Invoke the macro REGISTER_OP. This macro will
                        -
                      1. Call maker class to complete the proto and the checker
                        2. +
                      2. Call maker class to complete proto and checker
                      3. Using the completed proto and checker, it will add a new key-value pair to the OpInfoMap
                      • -
                    • Invoke the USE macro in which the Op is used, to make sure that it is linked.
                      • +
                    • Invoke the USE macro in which the Op is used to make sure that it is linked.
        @@ -429,13 +429,14 @@

        Build Backward Network

          -
        • Input: graph of forward operators
          • -
        • Output: graph of backward operators
          • +
        • Input: a graph of forward operators
          • +
        • Output: a 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
            • +
          • RNN Op => recursively call Backward on stepnet
        @@ -451,12 +452,12 @@
      2. Variable length Tensor design LoDTensor
        3. -
      3. Variable instances are the inputs and the outputs of an operator. Not just Tensor.
          +
        • 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 is where variables are stored.
              +
            • map<string var name, Variable>
            • Scope has a hierarchical structure. The local scope can get variables from its parent scope.
            • @@ -503,7 +504,7 @@

            Control the migration quality

            • Compare the performance of migrated models with old ones.
              • -
            • Follow the google C++ style
              • +
            • Follow the google C++ style guide.
            • 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.
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\ No newline at end of file diff --git a/develop/doc_cn/_sources/design/refactorization.md.txt b/develop/doc_cn/_sources/design/refactorization.md.txt index 629422e7743af666b42fd69fbff442ce15bef596..ec51aa1a0ec667175ff7215dcd359023e296769f 100644 --- a/develop/doc_cn/_sources/design/refactorization.md.txt +++ b/develop/doc_cn/_sources/design/refactorization.md.txt @@ -17,22 +17,22 @@ The goals of refactoring include: 1. A graph is composed of *variables* and *operators*. -1. The description of graphs must be capable of being serialized/deserialized, so that: +1. The description of graphs must be serializable/deserializable, so that: - 1. It can to be sent to the cloud for distributed execution, and + 1. It can 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 does the following steps +1. The Python program does two things - 1. *compilation*: run a Python program to generate a protobuf message representation of the graph and send it to + 1. *Compilation* runs 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*: 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. + 1. *Execution* executes 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 of Computation Graph -At compile time, the Python program generates a 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 a description of the graph. At runtime, the C++ program realizes the graph and runs it. @@ -42,11 +42,11 @@ At runtime, the C++ program realizes the graph and runs 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 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 `}`). +The word *graph* is interchangeable with *block* in this document. A graph consists of computation steps and local variables similar to a C++/Java program block, or a pair of parentheses(`{` and `}`). ## Compilation and Execution -1. Run an application Python program to describe the graph. In particular, the Python application program does the following: +1. Run a 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, @@ -54,10 +54,10 @@ The word *graph* is interchangeable with *block* in this document. A graph repr 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. Add optimization operators to the computation graph. + 1. Optionally, split the graph for distributed training. -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. The invocation of `train` or [`infer`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/inference.py#L108) methods in the 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. realize local variables defined in the BlockDesc message in the new scope, @@ -107,8 +107,8 @@ Compile Time -> IR -> Runtime ![class_diagram](http://api.paddlepaddle.org/graphviz?dot=https://gist.githubusercontent.com/reyoung/53df507f6749762675dff3e7ce53372f/raw/dd598e8f1976f5759f58af5e5ef94738a6b2e661/op.dot) * `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. + * Operator stores input/output variable names and attributes. + * The `InferShape` interface is used to infer the shape of the output variables based on the shapes of the input variables. * Use `Run` to compute the `output` variables from the `input` variables. --- @@ -139,7 +139,7 @@ Compile Time -> IR -> Runtime * 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`. + * `thrust`, in addition, supports more complex APIs, like `scan`, `reduce`, `reduce_by_key`. * Hand-writing `GPUKernel` and `CPU` 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.) --- @@ -185,10 +185,10 @@ Make sure the registration process is executed and linked. 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` + 1. Call maker class to complete `proto` and `checker` 2. Using the completed `proto` and `checker`, it will add a new key-value pair to the `OpInfoMap` -4. Invoke the `USE` macro in which the Op is used, to make sure that it is linked. +4. Invoke the `USE` macro in which the Op is used to make sure that it is linked. --- # Backward Module (1/2) @@ -199,13 +199,14 @@ Make sure the registration process is executed and linked. --- # Backward Module (2/2) ### Build Backward Network -- **Input**: graph of forward operators -- **Output**: graph of backward operators +- **Input**: a graph of forward operators +- **Output**: a 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 + - RNN Op => recursively call `Backward` on stepnet --- @@ -215,10 +216,10 @@ Make sure the registration process is executed and linked. * Only dims and data pointers are stored in `Tensor`. * 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`. +* `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 +* `Scope` is where variables are stored. + * map * `Scope` has a hierarchical structure. The local scope can get variables from its parent scope. --- @@ -246,7 +247,7 @@ Make sure the registration process is executed and linked. --- # Control the migration quality - Compare the performance of migrated models with old ones. -- Follow the google C++ style +- Follow the google C++ style guide. - 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. diff --git a/develop/doc_cn/design/refactorization.html b/develop/doc_cn/design/refactorization.html index 20af163b044c674b6c9bfff39a90ebf839b881a8..a8e61b16df6aece9d746cd6c48db01e125860f14 100644 --- a/develop/doc_cn/design/refactorization.html +++ b/develop/doc_cn/design/refactorization.html @@ -207,49 +207,49 @@
              • Please refer to computation graphs for a concrete example.
              • Users write Python programs to describe the graphs and run them (locally or remotely).
              • A graph is composed of variables and operators.
                • -
              • The description of graphs must be capable of being serialized/deserialized, so that:
                  -
                1. It can to be sent to the cloud for distributed execution, and
                  2. +
                2. The description of graphs must be serializable/deserializable, so that:
                    +
                  1. It can be sent to the cloud for distributed execution, and
                  2. It can be sent to clients for mobile or enterprise deployment.
                  3. -
                3. The Python program does the following steps
                    -
                  1. compilation: run a Python program to generate a protobuf message representation of the graph and send it to
                      +
                    1. The Python program does two things
                        +
                      1. Compilation runs 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,
                        2. the master process of a distributed training job for training, or
                        3. the server process of a Kubernetes serving job for distributed serving.
                        2. -
                      2. execution: execute the graph by constructing instances of class Variable and OperatorBase, according to the protobuf message.
                        3. +
                      3. Execution executes the graph by constructing instances of class Variable and OperatorBase, according to the protobuf message.

                    Description and Realization of Computation Graph

                    -

                    At compile time, the Python program generates a 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 a description of the graph.

                    At runtime, the C++ program realizes the graph and runs it.

                    | | Representation (protobuf messages) | Realization (C++ class objects) | |—|—|—| |Data|VarDesc|Variable| |Operation|OpDesc|Operator| |Block|BlockDesc|Block|

                    -

                    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 }).

                    +

                    The word graph is interchangeable with block in this document. A graph consists of computation steps and local variables similar to a C++/Java program block, or a pair of parentheses({ and }).

                    Compilation and Execution

                      -
                    1. Run an application Python program to describe the graph. In particular, the Python application program does the following:
                        +
                      1. Run a Python program to describe the graph. In particular, the Python application program does the following:
                        1. Create VarDesc to represent local/intermediate variables,
                        2. Create operators and set attributes,
                        3. Validate attribute values,
                        4. Infer the type and the shape of variables,
                        5. Plan memory-reuse for variables,
                        6. Generate the backward graph
                          7. -
                        7. Optimize the computation graph.
                          8. -
                        8. Potentially, split the graph for distributed training.
                          9. +
                        9. Add optimization operators to the computation graph.
                          10. +
                        10. Optionally, split the graph for distributed training.
                        2. -
                      2. The invocation of train or infer methods in the application Python program does the following:
                          +
                        1. The invocation of train or infer methods in the Python program does the following:
                          1. Create a new Scope instance in the scope hierarchy for each run of a block,
                            1. realize local variables defined in the BlockDesc message in the new scope,
                            2. a scope is similar to the stack frame in programming languages,
                              3. @@ -312,8 +312,8 @@

                              class_diagram

                              • 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.
                                  • +
                                • Operator stores input/output variable names and attributes.
                                  • +
                                • The InferShape interface is used to infer the shape of the output variables based on the shapes of the input variables.
                                • Use Run to compute the output variables from the input variables.
                                • @@ -357,7 +357,7 @@
                              • 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.
                                  • +
                                • thrust, in addition, supports more complex APIs, like scan, reduce, reduce_by_key.
                              • Hand-writing GPUKernel and CPU code
                                  @@ -419,11 +419,11 @@
                                • Write an Op class and its gradient Op class, if required.
                                • Write an Op maker class. In the constructor of this class, describe the inputs, outputs and attributes of the operator.
                                • Invoke the macro REGISTER_OP. This macro will
                                    -
                                  1. Call maker class to complete the proto and the checker
                                    2. +
                                  2. Call maker class to complete proto and checker
                                  3. Using the completed proto and checker, it will add a new key-value pair to the OpInfoMap
                                  • -
                                • Invoke the USE macro in which the Op is used, to make sure that it is linked.
                                  • +
                                • Invoke the USE macro in which the Op is used to make sure that it is linked.
                    @@ -443,13 +443,14 @@

                    Build Backward Network

                      -
                    • Input: graph of forward operators
                      • -
                    • Output: graph of backward operators
                      • +
                    • Input: a graph of forward operators
                      • +
                    • Output: a 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
                        • +
                      • RNN Op => recursively call Backward on stepnet
                    @@ -465,12 +466,12 @@
                  2. Variable length Tensor design LoDTensor
              • -
            • Variable instances are the inputs and the outputs of an operator. Not just Tensor.
                +
              • 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 is where variables are stored.
                    +
                  • map<string var name, Variable>
                  • Scope has a hierarchical structure. The local scope can get variables from its parent scope.
                  • @@ -517,7 +518,7 @@

                  Control the migration quality

                  • Compare the performance of migrated models with old ones.
                    • -
                  • Follow the google C++ style
                    • +
                  • Follow the google C++ style guide.
                  • 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.
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