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develop/doc/_sources/design/fluid.md.txt
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......@@ -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)
develop/doc/_sources/design/fluid_compiler.md.txt 0 → 100644
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# 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).
develop/doc/design/fluid.html
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......@@ -299,19 +299,13 @@
<span id="the-execution-of-a-fluid-program"></span><h2>The Execution of a Fluid Program<a class="headerlink" href="#the-execution-of-a-fluid-program" title="Permalink to this headline"></a></h2>
<p>There are two ways to execute a Fluid program. When a program is executed, it creates a protobuf message <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/a91efdde6910ce92a78e3aa7157412c4c88d9ee8/paddle/framework/framework.proto#L145"><code class="docutils literal"><span class="pre">ProgramDesc</span></code></a> that describes the process and is conceptually like an <a class="reference external" href="https://en.wikipedia.org/wiki/Abstract_syntax_tree">abstract syntax tree</a>.</p>
<p>There is a C++ class <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/executor.h"><code class="docutils literal"><span class="pre">Executor</span></code></a>, which runs a <code class="docutils literal"><span class="pre">ProgramDesc</span></code>, similar to how an interpreter runs a Python program.</p>
<p>Fluid is moving towards the direction of a compiler, which is explain in more detail later in this article.</p>
<p>Fluid is moving towards the direction of a compiler, which is explain in <a class="reference internal" href="fluid_compiler.html"><span class="doc">fluid</span></a>.</p>
</div>
<div class="section" id="backward-compatibility-of-fluid">
<span id="backward-compatibility-of-fluid"></span><h2>Backward Compatibility of Fluid<a class="headerlink" href="#backward-compatibility-of-fluid" title="Permalink to this headline"></a></h2>
<p>Given all the advantages from the removal of the concept of a <em>model</em>, 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 <a class="reference external" href="https://github.com/NervanaSystems/ngraph">n-graph</a>. Similarly, <a class="reference external" href="https://www.movidius.com/">Movidius</a> is producing a mobile deep learning chip that reads and runs graphs of operators. The well-known <a class="reference external" href="https://github.com/onnx/onnx">ONNX</a> is also a file format of graphs of operators.</p>
<p>For Fluid, we can write a converter that extracts the parts in the <code class="docutils literal"><span class="pre">ProgramDesc</span></code> protobuf message, converts them into a graph of operators, and exports the graph into the ONNX or n-graph format.</p>
</div>
<div class="section" id="towards-a-deep-learning-language-and-the-compiler">
<span id="towards-a-deep-learning-language-and-the-compiler"></span><h2>Towards a Deep Learning Language and the Compiler<a class="headerlink" href="#towards-a-deep-learning-language-and-the-compiler" title="Permalink to this headline"></a></h2>
<p>We can change the <code class="docutils literal"><span class="pre">if-then-else</span></code> and loop structure a little bit in the above Fluid example programs, to make it into a new programming language, different than Python.</p>
<p>Even if we do not invent a new language, as long as we get the <code class="docutils literal"><span class="pre">ProgramDesc</span></code> 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 <code class="docutils literal"><span class="pre">nvcc</span></code>. Another transpiler could generate MKL-friendly code that should be built using <code class="docutils literal"><span class="pre">icc</span></code> from Intel. More interestingly, we can translate a Fluid program into its distributed version of two <code class="docutils literal"><span class="pre">ProgramDesc</span></code> messages, one for running on the trainer process, and the other one for the parameter server. For more details of the last example, the <a class="reference internal" href="concurrent_programming.html"><span class="doc">concurrent programming design</span></a> document would be a good pointer. The following figure explains the proposed two-stage process:</p>
<p><img alt="" src="../_images/fluid-compiler.png" /></p>
</div>
</div>
......
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<li>PaddlePaddle Fluid: Towards a Compiled Programming Language</li>
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<div class="section" id="paddlepaddle-fluid-towards-a-compiled-programming-language">
<span id="paddlepaddle-fluid-towards-a-compiled-programming-language"></span><h1>PaddlePaddle Fluid: Towards a Compiled Programming Language<a class="headerlink" href="#paddlepaddle-fluid-towards-a-compiled-programming-language" title="Permalink to this headline"></a></h1>
<p>As described in <a class="reference internal" href="fluid.html"><span class="doc">fluid.md</span></a>, when a Fluid application program
runs, it generates a <code class="docutils literal"><span class="pre">ProgramDesc</span></code> protobuf message as an intermediate
representation of itself. The C++ class <code class="docutils literal"><span class="pre">Executor</span></code> can run this
protobuf message as an interpreter. This article describes the Fluid
compiler.</p>
<p><img alt="" src="../_images/fluid-compiler.png" /></p>
<div class="section" id="programdesc">
<span id="programdesc"></span><h2>ProgramDesc<a class="headerlink" href="#programdesc" title="Permalink to this headline"></a></h2>
<p>Before we go deeper into the idea of compiled language, let us take a
look at a simple example Fluid application.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="s2">&quot;fluid&quot;</span>
<span class="n">func</span> <span class="n">paddlepaddle</span><span class="p">()</span> <span class="p">{</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="n">W</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="n">Y</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">mult</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">W</span><span class="p">)</span>
<span class="p">}</span>
</pre></div>
</div>
<p>This program consists of a <a class="reference internal" href="block.html"><span class="doc">block</span></a> of three operators &#8211;
<code class="docutils literal"><span class="pre">read</span></code>, <code class="docutils literal"><span class="pre">assign</span></code>, and <code class="docutils literal"><span class="pre">mult</span></code>. Its <code class="docutils literal"><span class="pre">ProgramDesc</span></code> message looks like
the following</p>
<div class="highlight-protobuf"><div class="highlight"><pre><span></span><span class="kd">message</span> <span class="nc">ProgramDesc</span> <span class="p">{</span>
<span class="n">block</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">Block</span> <span class="p">{</span>
<span class="na">vars</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">,</span> <span class="n">W</span><span class="p">,</span> <span class="n">Y</span><span class="p">],</span>
<span class="na">ops</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">read</span><span class="p">(</span><span class="na">output</span> <span class="o">=</span> <span class="n">X</span><span class="p">)</span>
<span class="n">assign</span><span class="p">(</span><span class="na">input</span> <span class="o">=</span> <span class="o">...</span><span class="p">,</span> <span class="na">output</span> <span class="o">=</span> <span class="n">W</span><span class="p">)</span>
<span class="n">mult</span><span class="p">(</span><span class="na">input</span> <span class="o">=</span> <span class="p">{</span><span class="n">X</span><span class="p">,</span> <span class="n">W</span><span class="p">},</span> <span class="na">output</span> <span class="o">=</span> <span class="n">Y</span><span class="p">)</span>
<span class="p">],</span>
<span class="p">}</span>
<span class="p">}</span>
</pre></div>
</div>
</div>
<div class="section" id="transpilers">
<span id="transpilers"></span><h2>Transpilers<a class="headerlink" href="#transpilers" title="Permalink to this headline"></a></h2>
<p>We can write a transpiler program that takes a <code class="docutils literal"><span class="pre">ProgramDesc</span></code>, e.g.,
the above one, and outputs another <code class="docutils literal"><span class="pre">ProgramDesc</span></code>. Let us take some
examples:</p>
<ol class="simple">
<li><em>Memory optimization transpiler</em>: We can write a transpiler that
inserts some <code class="docutils literal"><span class="pre">FreeMemoryOp</span></code>s in the above example <code class="docutils literal"><span class="pre">ProgramDesc</span></code> so
to free memory early, before the end of an iteration, so to keep a
small memory footprint.</li>
<li><em>Distributed training transpiler</em>: We can write a transpiler that
converts a<code class="docutils literal"><span class="pre">ProgramDesc</span></code> into its distributed version of two
<code class="docutils literal"><span class="pre">ProgramDesc</span></code>s &#8211; one for running by the trainer processes and the
other for the parameter server.</li>
</ol>
<p>In the rest of this article, we talk about a special kind of
transpiler, <em>Native code generator</em>, which takes a <code class="docutils literal"><span class="pre">ProgramDesc</span></code> and
generates a <code class="docutils literal"><span class="pre">.cu</span></code> (or <code class="docutils literal"><span class="pre">.cc</span></code>) file, which could be built by C++
compilers (gcc, nvcc, icc) into binaries.</p>
</div>
<div class="section" id="native-code-generator">
<span id="native-code-generator"></span><h2>Native Code Generator<a class="headerlink" href="#native-code-generator" title="Permalink to this headline"></a></h2>
<p>For the above example, the native code generator transpiler, say, the
CUDA code generator, should generate a <code class="docutils literal"><span class="pre">main</span></code> function:</p>
<div class="highlight-c++"><div class="highlight"><pre><span></span><span class="kt">void</span> <span class="nf">main</span><span class="p">()</span> <span class="p">{</span>
<span class="k">auto</span> <span class="n">X</span> <span class="o">=</span> <span class="n">fluid_cuda_read</span><span class="p">(...);</span>
<span class="k">auto</span> <span class="n">W</span> <span class="o">=</span> <span class="n">fluid_cuda_create_tensor</span><span class="p">(...);</span>
<span class="k">auto</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">fluid_cuda_mult</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">W</span><span class="p">);</span>
<span class="p">}</span>
</pre></div>
</div>
<p>and the definitions of functions <code class="docutils literal"><span class="pre">fluid_cuda_read</span></code>,
<code class="docutils literal"><span class="pre">fluid_cuda_create_tensor</span></code>, and <code class="docutils literal"><span class="pre">fluid_cuda_mult</span></code>. Please be aware
that each function could just define a C++ instance of an operator and
run it. For example</p>
<div class="highlight-c++"><div class="highlight"><pre><span></span><span class="n">paddle</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">fluid_cuda_read</span><span class="p">(...)</span> <span class="p">{</span>
<span class="n">paddle</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">t</span><span class="p">;</span>
<span class="n">paddle</span><span class="o">::</span><span class="k">operator</span><span class="o">::</span><span class="n">Read</span> <span class="n">r</span><span class="p">(</span><span class="o">&amp;</span><span class="n">t</span><span class="p">,</span> <span class="p">...);</span>
<span class="n">r</span><span class="p">.</span><span class="n">Run</span><span class="p">();</span>
<span class="k">return</span> <span class="n">t</span><span class="p">;</span>
<span class="p">}</span>
</pre></div>
</div>
<p>For computational operators that have multiple <em>kernels</em>, each for a
specific hardware platform, for example, the <code class="docutils literal"><span class="pre">mult</span></code> operator, the
generated code should call its CUDA kernel:</p>
<div class="highlight-c++"><div class="highlight"><pre><span></span><span class="n">paddle</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">fluid_cuda_mult</span><span class="p">(</span><span class="k">const</span> <span class="n">paddle</span><span class="o">::</span><span class="n">Tensor</span><span class="o">&amp;</span> <span class="n">a</span><span class="p">,</span>
<span class="k">const</span> <span class="n">paddle</span><span class="o">::</span><span class="n">Tensor</span><span class="o">&amp;</span> <span class="n">b</span><span class="p">)</span> <span class="p">{</span>
<span class="n">paddle</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">t</span><span class="p">;</span>
<span class="n">paddle</span><span class="o">::</span><span class="k">operator</span><span class="o">::</span><span class="n">Mult</span> <span class="n">m</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="p">...);</span>
<span class="n">Mult</span><span class="p">.</span><span class="n">Run</span><span class="p">(</span><span class="n">cuda_context</span><span class="p">);</span>
<span class="p">}</span>
</pre></div>
</div>
<p>where <code class="docutils literal"><span class="pre">cuda_context</span></code> could be a global variable of type
<code class="docutils literal"><span class="pre">paddle::CUDADeviceContext</span></code>.</p>
</div>
<div class="section" id="multi-block-code-generation">
<span id="multi-block-code-generation"></span><h2>Multi-Block Code Generation<a class="headerlink" href="#multi-block-code-generation" title="Permalink to this headline"></a></h2>
<p>Most Fluid application programs may have more than one blocks. To
execute them, we need to trace <a class="reference internal" href="scope.html"><span class="doc">scopes</span></a>.</p>
</div>
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因为 它太大了无法显示 source diff 。你可以改为 查看blob。
develop/doc_cn/_sources/design/fluid.md.txt
浏览文件 @ c5a3067a
......@@ -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)
develop/doc_cn/_sources/design/fluid_compiler.md.txt 0 → 100644
浏览文件 @ c5a3067a
# 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).
develop/doc_cn/design/fluid.html
浏览文件 @ c5a3067a
......@@ -318,19 +318,13 @@
<span id="the-execution-of-a-fluid-program"></span><h2>The Execution of a Fluid Program<a class="headerlink" href="#the-execution-of-a-fluid-program" title="永久链接至标题"></a></h2>
<p>There are two ways to execute a Fluid program. When a program is executed, it creates a protobuf message <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/a91efdde6910ce92a78e3aa7157412c4c88d9ee8/paddle/framework/framework.proto#L145"><code class="docutils literal"><span class="pre">ProgramDesc</span></code></a> that describes the process and is conceptually like an <a class="reference external" href="https://en.wikipedia.org/wiki/Abstract_syntax_tree">abstract syntax tree</a>.</p>
<p>There is a C++ class <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/executor.h"><code class="docutils literal"><span class="pre">Executor</span></code></a>, which runs a <code class="docutils literal"><span class="pre">ProgramDesc</span></code>, similar to how an interpreter runs a Python program.</p>
<p>Fluid is moving towards the direction of a compiler, which is explain in more detail later in this article.</p>
<p>Fluid is moving towards the direction of a compiler, which is explain in <a class="reference internal" href="fluid_compiler.html"><span class="doc">fluid</span></a>.</p>
</div>
<div class="section" id="backward-compatibility-of-fluid">
<span id="backward-compatibility-of-fluid"></span><h2>Backward Compatibility of Fluid<a class="headerlink" href="#backward-compatibility-of-fluid" title="永久链接至标题"></a></h2>
<p>Given all the advantages from the removal of the concept of a <em>model</em>, 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 <a class="reference external" href="https://github.com/NervanaSystems/ngraph">n-graph</a>. Similarly, <a class="reference external" href="https://www.movidius.com/">Movidius</a> is producing a mobile deep learning chip that reads and runs graphs of operators. The well-known <a class="reference external" href="https://github.com/onnx/onnx">ONNX</a> is also a file format of graphs of operators.</p>
<p>For Fluid, we can write a converter that extracts the parts in the <code class="docutils literal"><span class="pre">ProgramDesc</span></code> protobuf message, converts them into a graph of operators, and exports the graph into the ONNX or n-graph format.</p>
</div>
<div class="section" id="towards-a-deep-learning-language-and-the-compiler">
<span id="towards-a-deep-learning-language-and-the-compiler"></span><h2>Towards a Deep Learning Language and the Compiler<a class="headerlink" href="#towards-a-deep-learning-language-and-the-compiler" title="永久链接至标题"></a></h2>
<p>We can change the <code class="docutils literal"><span class="pre">if-then-else</span></code> and loop structure a little bit in the above Fluid example programs, to make it into a new programming language, different than Python.</p>
<p>Even if we do not invent a new language, as long as we get the <code class="docutils literal"><span class="pre">ProgramDesc</span></code> 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 <code class="docutils literal"><span class="pre">nvcc</span></code>. Another transpiler could generate MKL-friendly code that should be built using <code class="docutils literal"><span class="pre">icc</span></code> from Intel. More interestingly, we can translate a Fluid program into its distributed version of two <code class="docutils literal"><span class="pre">ProgramDesc</span></code> messages, one for running on the trainer process, and the other one for the parameter server. For more details of the last example, the <a class="reference internal" href="concurrent_programming.html"><span class="doc">concurrent programming design</span></a> document would be a good pointer. The following figure explains the proposed two-stage process:</p>
<p><img alt="" src="../_images/fluid-compiler.png" /></p>
</div>
</div>
......
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<li>PaddlePaddle Fluid: Towards a Compiled Programming Language</li>
</ul>
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<div class="section" id="paddlepaddle-fluid-towards-a-compiled-programming-language">
<span id="paddlepaddle-fluid-towards-a-compiled-programming-language"></span><h1>PaddlePaddle Fluid: Towards a Compiled Programming Language<a class="headerlink" href="#paddlepaddle-fluid-towards-a-compiled-programming-language" title="永久链接至标题"></a></h1>
<p>As described in <a class="reference internal" href="fluid.html"><span class="doc">fluid.md</span></a>, when a Fluid application program
runs, it generates a <code class="docutils literal"><span class="pre">ProgramDesc</span></code> protobuf message as an intermediate
representation of itself. The C++ class <code class="docutils literal"><span class="pre">Executor</span></code> can run this
protobuf message as an interpreter. This article describes the Fluid
compiler.</p>
<p><img alt="" src="../_images/fluid-compiler.png" /></p>
<div class="section" id="programdesc">
<span id="programdesc"></span><h2>ProgramDesc<a class="headerlink" href="#programdesc" title="永久链接至标题"></a></h2>
<p>Before we go deeper into the idea of compiled language, let us take a
look at a simple example Fluid application.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="s2">&quot;fluid&quot;</span>
<span class="n">func</span> <span class="n">paddlepaddle</span><span class="p">()</span> <span class="p">{</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="n">W</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="n">Y</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">mult</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">W</span><span class="p">)</span>
<span class="p">}</span>
</pre></div>
</div>
<p>This program consists of a <a class="reference internal" href="block.html"><span class="doc">block</span></a> of three operators &#8211;
<code class="docutils literal"><span class="pre">read</span></code>, <code class="docutils literal"><span class="pre">assign</span></code>, and <code class="docutils literal"><span class="pre">mult</span></code>. Its <code class="docutils literal"><span class="pre">ProgramDesc</span></code> message looks like
the following</p>
<div class="highlight-protobuf"><div class="highlight"><pre><span></span><span class="kd">message</span> <span class="nc">ProgramDesc</span> <span class="p">{</span>
<span class="n">block</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">Block</span> <span class="p">{</span>
<span class="na">vars</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">,</span> <span class="n">W</span><span class="p">,</span> <span class="n">Y</span><span class="p">],</span>
<span class="na">ops</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">read</span><span class="p">(</span><span class="na">output</span> <span class="o">=</span> <span class="n">X</span><span class="p">)</span>
<span class="n">assign</span><span class="p">(</span><span class="na">input</span> <span class="o">=</span> <span class="o">...</span><span class="p">,</span> <span class="na">output</span> <span class="o">=</span> <span class="n">W</span><span class="p">)</span>
<span class="n">mult</span><span class="p">(</span><span class="na">input</span> <span class="o">=</span> <span class="p">{</span><span class="n">X</span><span class="p">,</span> <span class="n">W</span><span class="p">},</span> <span class="na">output</span> <span class="o">=</span> <span class="n">Y</span><span class="p">)</span>
<span class="p">],</span>
<span class="p">}</span>
<span class="p">}</span>
</pre></div>
</div>
</div>
<div class="section" id="transpilers">
<span id="transpilers"></span><h2>Transpilers<a class="headerlink" href="#transpilers" title="永久链接至标题"></a></h2>
<p>We can write a transpiler program that takes a <code class="docutils literal"><span class="pre">ProgramDesc</span></code>, e.g.,
the above one, and outputs another <code class="docutils literal"><span class="pre">ProgramDesc</span></code>. Let us take some
examples:</p>
<ol class="simple">
<li><em>Memory optimization transpiler</em>: We can write a transpiler that
inserts some <code class="docutils literal"><span class="pre">FreeMemoryOp</span></code>s in the above example <code class="docutils literal"><span class="pre">ProgramDesc</span></code> so
to free memory early, before the end of an iteration, so to keep a
small memory footprint.</li>
<li><em>Distributed training transpiler</em>: We can write a transpiler that
converts a<code class="docutils literal"><span class="pre">ProgramDesc</span></code> into its distributed version of two
<code class="docutils literal"><span class="pre">ProgramDesc</span></code>s &#8211; one for running by the trainer processes and the
other for the parameter server.</li>
</ol>
<p>In the rest of this article, we talk about a special kind of
transpiler, <em>Native code generator</em>, which takes a <code class="docutils literal"><span class="pre">ProgramDesc</span></code> and
generates a <code class="docutils literal"><span class="pre">.cu</span></code> (or <code class="docutils literal"><span class="pre">.cc</span></code>) file, which could be built by C++
compilers (gcc, nvcc, icc) into binaries.</p>
</div>
<div class="section" id="native-code-generator">
<span id="native-code-generator"></span><h2>Native Code Generator<a class="headerlink" href="#native-code-generator" title="永久链接至标题"></a></h2>
<p>For the above example, the native code generator transpiler, say, the
CUDA code generator, should generate a <code class="docutils literal"><span class="pre">main</span></code> function:</p>
<div class="highlight-c++"><div class="highlight"><pre><span></span><span class="kt">void</span> <span class="nf">main</span><span class="p">()</span> <span class="p">{</span>
<span class="k">auto</span> <span class="n">X</span> <span class="o">=</span> <span class="n">fluid_cuda_read</span><span class="p">(...);</span>
<span class="k">auto</span> <span class="n">W</span> <span class="o">=</span> <span class="n">fluid_cuda_create_tensor</span><span class="p">(...);</span>
<span class="k">auto</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">fluid_cuda_mult</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">W</span><span class="p">);</span>
<span class="p">}</span>
</pre></div>
</div>
<p>and the definitions of functions <code class="docutils literal"><span class="pre">fluid_cuda_read</span></code>,
<code class="docutils literal"><span class="pre">fluid_cuda_create_tensor</span></code>, and <code class="docutils literal"><span class="pre">fluid_cuda_mult</span></code>. Please be aware
that each function could just define a C++ instance of an operator and
run it. For example</p>
<div class="highlight-c++"><div class="highlight"><pre><span></span><span class="n">paddle</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">fluid_cuda_read</span><span class="p">(...)</span> <span class="p">{</span>
<span class="n">paddle</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">t</span><span class="p">;</span>
<span class="n">paddle</span><span class="o">::</span><span class="k">operator</span><span class="o">::</span><span class="n">Read</span> <span class="n">r</span><span class="p">(</span><span class="o">&amp;</span><span class="n">t</span><span class="p">,</span> <span class="p">...);</span>
<span class="n">r</span><span class="p">.</span><span class="n">Run</span><span class="p">();</span>
<span class="k">return</span> <span class="n">t</span><span class="p">;</span>
<span class="p">}</span>
</pre></div>
</div>
<p>For computational operators that have multiple <em>kernels</em>, each for a
specific hardware platform, for example, the <code class="docutils literal"><span class="pre">mult</span></code> operator, the
generated code should call its CUDA kernel:</p>
<div class="highlight-c++"><div class="highlight"><pre><span></span><span class="n">paddle</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">fluid_cuda_mult</span><span class="p">(</span><span class="k">const</span> <span class="n">paddle</span><span class="o">::</span><span class="n">Tensor</span><span class="o">&amp;</span> <span class="n">a</span><span class="p">,</span>
<span class="k">const</span> <span class="n">paddle</span><span class="o">::</span><span class="n">Tensor</span><span class="o">&amp;</span> <span class="n">b</span><span class="p">)</span> <span class="p">{</span>
<span class="n">paddle</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">t</span><span class="p">;</span>
<span class="n">paddle</span><span class="o">::</span><span class="k">operator</span><span class="o">::</span><span class="n">Mult</span> <span class="n">m</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="p">...);</span>
<span class="n">Mult</span><span class="p">.</span><span class="n">Run</span><span class="p">(</span><span class="n">cuda_context</span><span class="p">);</span>
<span class="p">}</span>
</pre></div>
</div>
<p>where <code class="docutils literal"><span class="pre">cuda_context</span></code> could be a global variable of type
<code class="docutils literal"><span class="pre">paddle::CUDADeviceContext</span></code>.</p>
</div>
<div class="section" id="multi-block-code-generation">
<span id="multi-block-code-generation"></span><h2>Multi-Block Code Generation<a class="headerlink" href="#multi-block-code-generation" title="永久链接至标题"></a></h2>
<p>Most Fluid application programs may have more than one blocks. To
execute them, we need to trace <a class="reference internal" href="scope.html"><span class="doc">scopes</span></a>.</p>
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