diff --git a/develop/doc/_sources/design/fluid.md.txt b/develop/doc/_sources/design/fluid.md.txt index 585dc8ef39c0cfb30f470d79f7b27a59ceb5e940..2acc168007d25a083f588b48f84e12e29baf4f47 100644 --- a/develop/doc/_sources/design/fluid.md.txt +++ b/develop/doc/_sources/design/fluid.md.txt @@ -105,18 +105,10 @@ There are two ways to execute a Fluid program. When a program is executed, it c There is a C++ class [`Executor`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/executor.h), which runs a `ProgramDesc`, similar to how an interpreter runs a Python program. -Fluid is moving towards the direction of a compiler, which is explain in more detail later in this article. +Fluid is moving towards the direction of a compiler, which is explain in [fluid_compiler.md](fluid_compiler.md). ## Backward Compatibility of Fluid Given all the advantages from the removal of the concept of a *model*, hardware manufacturers might still prefer the existence of the concept of a model, so it would be easier for them to support multiple frameworks all at once and could run a trained model during inference. For example, Nervana, a startup company acquired by Intel, has been working on an XPU that reads the models in the format known as [n-graph](https://github.com/NervanaSystems/ngraph). Similarly, [Movidius](https://www.movidius.com/) is producing a mobile deep learning chip that reads and runs graphs of operators. The well-known [ONNX](https://github.com/onnx/onnx) is also a file format of graphs of operators. For Fluid, we can write a converter that extracts the parts in the `ProgramDesc` protobuf message, converts them into a graph of operators, and exports the graph into the ONNX or n-graph format. - -## Towards a Deep Learning Language and the Compiler - -We can change the `if-then-else` and loop structure a little bit in the above Fluid example programs, to make it into a new programming language, different than Python. - -Even if we do not invent a new language, as long as we get the `ProgramDesc` message filled in, we can write a transpiler, which translates each invocation to an operator, into a C++ call to a kernel function of that operator. For example, a transpiler that weaves the CUDA kernels outputs an NVIDIA-friendly C++ program, which can be built using `nvcc`. Another transpiler could generate MKL-friendly code that should be built using `icc` from Intel. More interestingly, we can translate a Fluid program into its distributed version of two `ProgramDesc` messages, one for running on the trainer process, and the other one for the parameter server. For more details of the last example, the [concurrent programming design](concurrent_programming.md) document would be a good pointer. The following figure explains the proposed two-stage process: - -![](fluid-compiler.png) diff --git a/develop/doc/_sources/design/fluid_compiler.md.txt b/develop/doc/_sources/design/fluid_compiler.md.txt new file mode 100644 index 0000000000000000000000000000000000000000..2a6beafc52e815fa067b273bb5887ddcf6ab15ae --- /dev/null +++ b/develop/doc/_sources/design/fluid_compiler.md.txt @@ -0,0 +1,110 @@ +# PaddlePaddle Fluid: Towards a Compiled Programming Language + +As described in [fluid.md](fluid.md), when a Fluid application program +runs, it generates a `ProgramDesc` protobuf message as an intermediate +representation of itself. The C++ class `Executor` can run this +protobuf message as an interpreter. This article describes the Fluid +compiler. + +![](fluid-compiler.png) + +## ProgramDesc + +Before we go deeper into the idea of compiled language, let us take a +look at a simple example Fluid application. + +```python +import "fluid" + +func paddlepaddle() { + X = fluid.read(...) + W = fluid.Tensor(...) + Y = fluid.mult(X, W) +} +``` + +This program consists of a [block](block.md) of three operators -- +`read`, `assign`, and `mult`. Its `ProgramDesc` message looks like +the following + +```protobuf +message ProgramDesc { + block[0] = Block { + vars = [X, W, Y], + ops = [ + read(output = X) + assign(input = ..., output = W) + mult(input = {X, W}, output = Y) + ], + } +} +``` + +## Transpilers + +We can write a transpiler program that takes a `ProgramDesc`, e.g., +the above one, and outputs another `ProgramDesc`. Let us take some +examples: + +1. *Memory optimization transpiler*: We can write a transpiler that + inserts some `FreeMemoryOp`s in the above example `ProgramDesc` so + to free memory early, before the end of an iteration, so to keep a + small memory footprint. + +1. *Distributed training transpiler*: We can write a transpiler that + converts a`ProgramDesc` into its distributed version of two + `ProgramDesc`s -- one for running by the trainer processes and the + other for the parameter server. + +In the rest of this article, we talk about a special kind of +transpiler, *Native code generator*, which takes a `ProgramDesc` and +generates a `.cu` (or `.cc`) file, which could be built by C++ +compilers (gcc, nvcc, icc) into binaries. + +## Native Code Generator + +For the above example, the native code generator transpiler, say, the +CUDA code generator, should generate a `main` function: + +```c++ +void main() { + auto X = fluid_cuda_read(...); + auto W = fluid_cuda_create_tensor(...); + auto Y = fluid_cuda_mult(X, W); +} +``` + +and the definitions of functions `fluid_cuda_read`, +`fluid_cuda_create_tensor`, and `fluid_cuda_mult`. Please be aware +that each function could just define a C++ instance of an operator and +run it. For example + +```c++ +paddle::Tensor fluid_cuda_read(...) { + paddle::Tensor t; + paddle::operator::Read r(&t, ...); + r.Run(); + return t; +} +``` + +For computational operators that have multiple *kernels*, each for a +specific hardware platform, for example, the `mult` operator, the +generated code should call its CUDA kernel: + +```c++ +paddle::Tensor fluid_cuda_mult(const paddle::Tensor& a, + const paddle::Tensor& b) { + paddle::Tensor t; + paddle::operator::Mult m(a, b, ...); + Mult.Run(cuda_context); +} +``` + +where `cuda_context` could be a global variable of type +`paddle::CUDADeviceContext`. + +## Multi-Block Code Generation + +Most Fluid application programs may have more than one blocks. To +execute them, we need to trace [scopes](scope.md). diff --git a/develop/doc/design/fluid.html b/develop/doc/design/fluid.html index 784dbb81bce6c5b7846c7b1e623c016ffc3b117d..67f5df790bb75cc5315ec6ae06d110496249cce4 100644 --- a/develop/doc/design/fluid.html +++ b/develop/doc/design/fluid.html @@ -299,19 +299,13 @@

The Execution of a Fluid Program

There are two ways to execute a Fluid program. When a program is executed, it creates a protobuf message ProgramDesc that describes the process and is conceptually like an abstract syntax tree.

There is a C++ class Executor, which runs a ProgramDesc, similar to how an interpreter runs a Python program.

-

Fluid is moving towards the direction of a compiler, which is explain in more detail later in this article.

+

Fluid is moving towards the direction of a compiler, which is explain in fluid.

Backward Compatibility of Fluid

Given all the advantages from the removal of the concept of a model, hardware manufacturers might still prefer the existence of the concept of a model, so it would be easier for them to support multiple frameworks all at once and could run a trained model during inference. For example, Nervana, a startup company acquired by Intel, has been working on an XPU that reads the models in the format known as n-graph. Similarly, Movidius is producing a mobile deep learning chip that reads and runs graphs of operators. The well-known ONNX is also a file format of graphs of operators.

For Fluid, we can write a converter that extracts the parts in the ProgramDesc protobuf message, converts them into a graph of operators, and exports the graph into the ONNX or n-graph format.

-
-

Towards a Deep Learning Language and the Compiler

-

We can change the if-then-else and loop structure a little bit in the above Fluid example programs, to make it into a new programming language, different than Python.

-

Even if we do not invent a new language, as long as we get the ProgramDesc message filled in, we can write a transpiler, which translates each invocation to an operator, into a C++ call to a kernel function of that operator. For example, a transpiler that weaves the CUDA kernels outputs an NVIDIA-friendly C++ program, which can be built using nvcc. Another transpiler could generate MKL-friendly code that should be built using icc from Intel. More interestingly, we can translate a Fluid program into its distributed version of two ProgramDesc messages, one for running on the trainer process, and the other one for the parameter server. For more details of the last example, the concurrent programming design document would be a good pointer. The following figure explains the proposed two-stage process:

-

-
diff --git a/develop/doc/design/fluid_compiler.html b/develop/doc/design/fluid_compiler.html new file mode 100644 index 0000000000000000000000000000000000000000..b6f0139d18cf64171655c78d86307f7868acece9 --- /dev/null +++ b/develop/doc/design/fluid_compiler.html @@ -0,0 +1,369 @@ + + + + + + + + + + + PaddlePaddle Fluid: Towards a Compiled Programming Language — PaddlePaddle documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+ Fork me on Github +
+ + English + + + + +
+ +
+
+ + + + +
+
+ + + +
+
+
+
+
+ +
+ + + + +
+ + + + + + + + + + + +
+
    + +
  • PaddlePaddle Fluid: Towards a Compiled Programming Language
    • +
+
+ +
+
+
+
+ +
+

PaddlePaddle Fluid: Towards a Compiled Programming Language

+

As described in fluid.md, when a Fluid application program +runs, it generates a ProgramDesc protobuf message as an intermediate +representation of itself. The C++ class Executor can run this +protobuf message as an interpreter. This article describes the Fluid +compiler.

+

+
+

ProgramDesc

+

Before we go deeper into the idea of compiled language, let us take a +look at a simple example Fluid application.

+
import "fluid"
+
+func paddlepaddle() {
+  X = fluid.read(...)
+  W = fluid.Tensor(...)
+  Y = fluid.mult(X, W)
+}
+
+
+

This program consists of a block of three operators – +read, assign, and mult. Its ProgramDesc message looks like +the following

+
message ProgramDesc {
+  block[0] = Block {
+    vars = [X, W, Y],
+    ops = [
+      read(output = X)
+      assign(input = ..., output = W)
+      mult(input = {X, W}, output = Y)
+    ],
+  }
+}
+
+
+
+
+

Transpilers

+

We can write a transpiler program that takes a ProgramDesc, e.g., +the above one, and outputs another ProgramDesc. Let us take some +examples:

+
    +
  1. Memory optimization transpiler: We can write a transpiler that +inserts some FreeMemoryOps in the above example ProgramDesc so +to free memory early, before the end of an iteration, so to keep a +small memory footprint.
    2. +
  2. Distributed training transpiler: We can write a transpiler that +converts aProgramDesc into its distributed version of two +ProgramDescs – one for running by the trainer processes and the +other for the parameter server.
    3. +
+

In the rest of this article, we talk about a special kind of +transpiler, Native code generator, which takes a ProgramDesc and +generates a .cu (or .cc) file, which could be built by C++ +compilers (gcc, nvcc, icc) into binaries.

+
+
+

Native Code Generator

+

For the above example, the native code generator transpiler, say, the +CUDA code generator, should generate a main function:

+
void main() {
+  auto X = fluid_cuda_read(...);
+  auto W = fluid_cuda_create_tensor(...);
+  auto Y = fluid_cuda_mult(X, W);
+}
+
+
+

and the definitions of functions fluid_cuda_read, +fluid_cuda_create_tensor, and fluid_cuda_mult. Please be aware +that each function could just define a C++ instance of an operator and +run it. For example

+
paddle::Tensor fluid_cuda_read(...) {
+  paddle::Tensor t;
+  paddle::operator::Read r(&t, ...);
+  r.Run();
+  return t;
+}
+
+
+

For computational operators that have multiple kernels, each for a +specific hardware platform, for example, the mult operator, the +generated code should call its CUDA kernel:

+
paddle::Tensor fluid_cuda_mult(const paddle::Tensor& a, 
+                               const paddle::Tensor& b) {
+  paddle::Tensor t;
+  paddle::operator::Mult m(a, b, ...);
+  Mult.Run(cuda_context);
+}
+
+
+

where cuda_context could be a global variable of type +paddle::CUDADeviceContext.

+
+
+

Multi-Block Code Generation

+

Most Fluid application programs may have more than one blocks. To +execute them, we need to trace scopes.

+
+
+ + +
+
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/develop/doc/objects.inv b/develop/doc/objects.inv index 23ccdfa0dd2034e6a27670c95982c36558dd8db5..21f9459635cae0413dc0970a93b8b3e4c1b6ef05 100644 Binary files a/develop/doc/objects.inv and b/develop/doc/objects.inv differ diff --git a/develop/doc/searchindex.js b/develop/doc/searchindex.js index 32f03823136deca3214bae598d643141b3a4ffd8..bf52e3e307243f48a6fe017e81f6d91fb97ee4ea 100644 --- a/develop/doc/searchindex.js +++ b/develop/doc/searchindex.js @@ -1 +1 @@ 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\ No newline at end of file diff --git a/develop/doc_cn/_sources/design/fluid.md.txt b/develop/doc_cn/_sources/design/fluid.md.txt index 585dc8ef39c0cfb30f470d79f7b27a59ceb5e940..2acc168007d25a083f588b48f84e12e29baf4f47 100644 --- a/develop/doc_cn/_sources/design/fluid.md.txt +++ b/develop/doc_cn/_sources/design/fluid.md.txt @@ -105,18 +105,10 @@ There are two ways to execute a Fluid program. When a program is executed, it c There is a C++ class [`Executor`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/executor.h), which runs a `ProgramDesc`, similar to how an interpreter runs a Python program. -Fluid is moving towards the direction of a compiler, which is explain in more detail later in this article. +Fluid is moving towards the direction of a compiler, which is explain in [fluid_compiler.md](fluid_compiler.md). ## Backward Compatibility of Fluid Given all the advantages from the removal of the concept of a *model*, hardware manufacturers might still prefer the existence of the concept of a model, so it would be easier for them to support multiple frameworks all at once and could run a trained model during inference. For example, Nervana, a startup company acquired by Intel, has been working on an XPU that reads the models in the format known as [n-graph](https://github.com/NervanaSystems/ngraph). Similarly, [Movidius](https://www.movidius.com/) is producing a mobile deep learning chip that reads and runs graphs of operators. The well-known [ONNX](https://github.com/onnx/onnx) is also a file format of graphs of operators. For Fluid, we can write a converter that extracts the parts in the `ProgramDesc` protobuf message, converts them into a graph of operators, and exports the graph into the ONNX or n-graph format. - -## Towards a Deep Learning Language and the Compiler - -We can change the `if-then-else` and loop structure a little bit in the above Fluid example programs, to make it into a new programming language, different than Python. - -Even if we do not invent a new language, as long as we get the `ProgramDesc` message filled in, we can write a transpiler, which translates each invocation to an operator, into a C++ call to a kernel function of that operator. For example, a transpiler that weaves the CUDA kernels outputs an NVIDIA-friendly C++ program, which can be built using `nvcc`. Another transpiler could generate MKL-friendly code that should be built using `icc` from Intel. More interestingly, we can translate a Fluid program into its distributed version of two `ProgramDesc` messages, one for running on the trainer process, and the other one for the parameter server. For more details of the last example, the [concurrent programming design](concurrent_programming.md) document would be a good pointer. The following figure explains the proposed two-stage process: - -![](fluid-compiler.png) diff --git a/develop/doc_cn/_sources/design/fluid_compiler.md.txt b/develop/doc_cn/_sources/design/fluid_compiler.md.txt new file mode 100644 index 0000000000000000000000000000000000000000..2a6beafc52e815fa067b273bb5887ddcf6ab15ae --- /dev/null +++ b/develop/doc_cn/_sources/design/fluid_compiler.md.txt @@ -0,0 +1,110 @@ +# PaddlePaddle Fluid: Towards a Compiled Programming Language + +As described in [fluid.md](fluid.md), when a Fluid application program +runs, it generates a `ProgramDesc` protobuf message as an intermediate +representation of itself. The C++ class `Executor` can run this +protobuf message as an interpreter. This article describes the Fluid +compiler. + +![](fluid-compiler.png) + +## ProgramDesc + +Before we go deeper into the idea of compiled language, let us take a +look at a simple example Fluid application. + +```python +import "fluid" + +func paddlepaddle() { + X = fluid.read(...) + W = fluid.Tensor(...) + Y = fluid.mult(X, W) +} +``` + +This program consists of a [block](block.md) of three operators -- +`read`, `assign`, and `mult`. Its `ProgramDesc` message looks like +the following + +```protobuf +message ProgramDesc { + block[0] = Block { + vars = [X, W, Y], + ops = [ + read(output = X) + assign(input = ..., output = W) + mult(input = {X, W}, output = Y) + ], + } +} +``` + +## Transpilers + +We can write a transpiler program that takes a `ProgramDesc`, e.g., +the above one, and outputs another `ProgramDesc`. Let us take some +examples: + +1. *Memory optimization transpiler*: We can write a transpiler that + inserts some `FreeMemoryOp`s in the above example `ProgramDesc` so + to free memory early, before the end of an iteration, so to keep a + small memory footprint. + +1. *Distributed training transpiler*: We can write a transpiler that + converts a`ProgramDesc` into its distributed version of two + `ProgramDesc`s -- one for running by the trainer processes and the + other for the parameter server. + +In the rest of this article, we talk about a special kind of +transpiler, *Native code generator*, which takes a `ProgramDesc` and +generates a `.cu` (or `.cc`) file, which could be built by C++ +compilers (gcc, nvcc, icc) into binaries. + +## Native Code Generator + +For the above example, the native code generator transpiler, say, the +CUDA code generator, should generate a `main` function: + +```c++ +void main() { + auto X = fluid_cuda_read(...); + auto W = fluid_cuda_create_tensor(...); + auto Y = fluid_cuda_mult(X, W); +} +``` + +and the definitions of functions `fluid_cuda_read`, +`fluid_cuda_create_tensor`, and `fluid_cuda_mult`. Please be aware +that each function could just define a C++ instance of an operator and +run it. For example + +```c++ +paddle::Tensor fluid_cuda_read(...) { + paddle::Tensor t; + paddle::operator::Read r(&t, ...); + r.Run(); + return t; +} +``` + +For computational operators that have multiple *kernels*, each for a +specific hardware platform, for example, the `mult` operator, the +generated code should call its CUDA kernel: + +```c++ +paddle::Tensor fluid_cuda_mult(const paddle::Tensor& a, + const paddle::Tensor& b) { + paddle::Tensor t; + paddle::operator::Mult m(a, b, ...); + Mult.Run(cuda_context); +} +``` + +where `cuda_context` could be a global variable of type +`paddle::CUDADeviceContext`. + +## Multi-Block Code Generation + +Most Fluid application programs may have more than one blocks. To +execute them, we need to trace [scopes](scope.md). diff --git a/develop/doc_cn/design/fluid.html b/develop/doc_cn/design/fluid.html index 26bdd459499b21ff83ed03cb8530da98098f9891..7d20694f7f1dd3e9cd8f4799a77dbfd06212a74f 100644 --- a/develop/doc_cn/design/fluid.html +++ b/develop/doc_cn/design/fluid.html @@ -318,19 +318,13 @@

The Execution of a Fluid Program

There are two ways to execute a Fluid program. When a program is executed, it creates a protobuf message ProgramDesc that describes the process and is conceptually like an abstract syntax tree.

There is a C++ class Executor, which runs a ProgramDesc, similar to how an interpreter runs a Python program.

-

Fluid is moving towards the direction of a compiler, which is explain in more detail later in this article.

+

Fluid is moving towards the direction of a compiler, which is explain in fluid.

Backward Compatibility of Fluid

Given all the advantages from the removal of the concept of a model, hardware manufacturers might still prefer the existence of the concept of a model, so it would be easier for them to support multiple frameworks all at once and could run a trained model during inference. For example, Nervana, a startup company acquired by Intel, has been working on an XPU that reads the models in the format known as n-graph. Similarly, Movidius is producing a mobile deep learning chip that reads and runs graphs of operators. The well-known ONNX is also a file format of graphs of operators.

For Fluid, we can write a converter that extracts the parts in the ProgramDesc protobuf message, converts them into a graph of operators, and exports the graph into the ONNX or n-graph format.

-
-

Towards a Deep Learning Language and the Compiler

-

We can change the if-then-else and loop structure a little bit in the above Fluid example programs, to make it into a new programming language, different than Python.

-

Even if we do not invent a new language, as long as we get the ProgramDesc message filled in, we can write a transpiler, which translates each invocation to an operator, into a C++ call to a kernel function of that operator. For example, a transpiler that weaves the CUDA kernels outputs an NVIDIA-friendly C++ program, which can be built using nvcc. Another transpiler could generate MKL-friendly code that should be built using icc from Intel. More interestingly, we can translate a Fluid program into its distributed version of two ProgramDesc messages, one for running on the trainer process, and the other one for the parameter server. For more details of the last example, the concurrent programming design document would be a good pointer. The following figure explains the proposed two-stage process:

-

-
diff --git a/develop/doc_cn/design/fluid_compiler.html b/develop/doc_cn/design/fluid_compiler.html new file mode 100644 index 0000000000000000000000000000000000000000..f20d5e619f697d7feab8ac3fb1b0261234a2fcec --- /dev/null +++ b/develop/doc_cn/design/fluid_compiler.html @@ -0,0 +1,389 @@ + + + + + + + + + + + PaddlePaddle Fluid: Towards a Compiled Programming Language — PaddlePaddle 文档 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+ Fork me on Github +
+ + English + + + + +
+ +
+
+ + + + +
+
+ + + +
+
+
+
+
+ +
+ + + + +
+ + + + + + + + + + + +
+
    + +
  • PaddlePaddle Fluid: Towards a Compiled Programming Language
    • +
+
+ +
+
+
+
+ +
+

PaddlePaddle Fluid: Towards a Compiled Programming Language

+

As described in fluid.md, when a Fluid application program +runs, it generates a ProgramDesc protobuf message as an intermediate +representation of itself. The C++ class Executor can run this +protobuf message as an interpreter. This article describes the Fluid +compiler.

+

+
+

ProgramDesc

+

Before we go deeper into the idea of compiled language, let us take a +look at a simple example Fluid application.

+
import "fluid"
+
+func paddlepaddle() {
+  X = fluid.read(...)
+  W = fluid.Tensor(...)
+  Y = fluid.mult(X, W)
+}
+
+
+

This program consists of a block of three operators – +read, assign, and mult. Its ProgramDesc message looks like +the following

+
message ProgramDesc {
+  block[0] = Block {
+    vars = [X, W, Y],
+    ops = [
+      read(output = X)
+      assign(input = ..., output = W)
+      mult(input = {X, W}, output = Y)
+    ],
+  }
+}
+
+
+
+
+

Transpilers

+

We can write a transpiler program that takes a ProgramDesc, e.g., +the above one, and outputs another ProgramDesc. Let us take some +examples:

+
    +
  1. Memory optimization transpiler: We can write a transpiler that +inserts some FreeMemoryOps in the above example ProgramDesc so +to free memory early, before the end of an iteration, so to keep a +small memory footprint.
    2. +
  2. Distributed training transpiler: We can write a transpiler that +converts aProgramDesc into its distributed version of two +ProgramDescs – one for running by the trainer processes and the +other for the parameter server.
    3. +
+

In the rest of this article, we talk about a special kind of +transpiler, Native code generator, which takes a ProgramDesc and +generates a .cu (or .cc) file, which could be built by C++ +compilers (gcc, nvcc, icc) into binaries.

+
+
+

Native Code Generator

+

For the above example, the native code generator transpiler, say, the +CUDA code generator, should generate a main function:

+
void main() {
+  auto X = fluid_cuda_read(...);
+  auto W = fluid_cuda_create_tensor(...);
+  auto Y = fluid_cuda_mult(X, W);
+}
+
+
+

and the definitions of functions fluid_cuda_read, +fluid_cuda_create_tensor, and fluid_cuda_mult. Please be aware +that each function could just define a C++ instance of an operator and +run it. For example

+
paddle::Tensor fluid_cuda_read(...) {
+  paddle::Tensor t;
+  paddle::operator::Read r(&t, ...);
+  r.Run();
+  return t;
+}
+
+
+

For computational operators that have multiple kernels, each for a +specific hardware platform, for example, the mult operator, the +generated code should call its CUDA kernel:

+
paddle::Tensor fluid_cuda_mult(const paddle::Tensor& a, 
+                               const paddle::Tensor& b) {
+  paddle::Tensor t;
+  paddle::operator::Mult m(a, b, ...);
+  Mult.Run(cuda_context);
+}
+
+
+

where cuda_context could be a global variable of type +paddle::CUDADeviceContext.

+
+
+

Multi-Block Code Generation

+

Most Fluid application programs may have more than one blocks. To +execute them, we need to trace scopes.

+
+
+ + +
+
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/develop/doc_cn/objects.inv b/develop/doc_cn/objects.inv index e6fbcca73d468264d9181ece6cecc3719f128954..9b219d0a6c977fe9d34b0809161ea36aad8397b3 100644 Binary files a/develop/doc_cn/objects.inv and b/develop/doc_cn/objects.inv differ diff --git a/develop/doc_cn/searchindex.js b/develop/doc_cn/searchindex.js index dff557ec5de7d0c6fc7e72ba616dd17f786b3c41..ac7923dbfbce460dd2cf0bf9ae754c588e0a4fe3 100644 --- a/develop/doc_cn/searchindex.js +++ b/develop/doc_cn/searchindex.js @@ -1 +1 @@ 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