Skip to content

P
Paddle

PaddlePaddle / Paddle
大约 1 年 前同步成功

通知 2298
Star 20931
Fork 5422
P
Paddle
提交 20cb5f06 编写于 作者: T Travis CI
浏览文件
操作

Deploy to GitHub Pages: e82fda25

上级 b1435ec7
展开全部 隐藏空白更改
内联 并排
Showing with 44 addition and 100 deletion
develop/doc/_sources/howto/dev/new_op_en.md.txt
浏览文件 @ 20cb5f06
......@@ -293,41 +293,27 @@ class TestMulGradOp(GradientChecker):
'Y': np.random.random((84, 100)).astype("float32")
}
def test_cpu_gpu_compare(self):
self.compare_grad(self.op, self.inputs)
def test_normal(self):
def test_check_grad_normal(self):
# mul op will enlarge the relative error
self.check_grad(
self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5)
self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5)
def test_ignore_x(self):
def test_check_grad_ingore_x(self):
self.check_grad(
self.op,
self.inputs, ["Y"],
"Out",
max_relative_error=0.5,
no_grad_set={"X"})
['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X"))
def test_ignore_y(self):
def test_check_grad_ingore_y(self):
self.check_grad(
self.op,
self.inputs, ["X"],
"Out",
max_relative_error=0.5,
no_grad_set={"Y"})
['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
```
Some key points in the code above include:
- `create_op("mul")` creates the backward operator's corresponding forward operator.
- `compare_grad` compares results between utilizing the CPU and the GPU.
- `test_normal` calls `check_grad` to validate scaling tests' correctness and stability through numeric methods.
- The first variable `self.op` denotes the forward operator.
- The second variable `self.inputs` denotes the input dictionary, which has its key value identical to its `ProtoMaker` definitions.
- The third variable `["X", "Y"]` appoints `X` and `Y` to be scale tested.
- The fourth variable `"Out"` points to the network's final output target `Out`.
- `test_ignore_x` and `test_ignore_y`branches test the cases where there is only one scaling input.
- The first variable `["X", "Y"]` appoints `X` and `Y` to be scale tested.
- The second variable `"Out"` points to the network's final output target `Out`.
- The third variable `max_relative_error` points to the maximum relative tolerance error during scaling tests.
- `test_check_grad_ingore_x` and `test_check_grad_ingore_y`branches test the cases where there is only one scaling input.
### Compiling and Running
......
develop/doc/howto/dev/new_op_en.html
浏览文件 @ 20cb5f06
......@@ -445,43 +445,29 @@ Registering the Op | Ops are registered in <code class="docutils liter
<span class="s1">&#39;Y&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="mi">84</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;float32&quot;</span><span class="p">)</span>
<span class="p">}</span>
<span class="k">def</span> <span class="nf">test_cpu_gpu_compare</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">compare_grad</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">op</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">test_normal</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">test_check_grad_normal</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="c1"># mul op will enlarge the relative error</span>
<span class="bp">self</span><span class="o">.</span><span class="n">check_grad</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">op</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;X&quot;</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">],</span> <span class="s2">&quot;Out&quot;</span><span class="p">,</span> <span class="n">max_relative_error</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">check_grad</span><span class="p">([</span><span class="s1">&#39;X&#39;</span><span class="p">,</span> <span class="s1">&#39;Y&#39;</span><span class="p">],</span> <span class="s1">&#39;Out&#39;</span><span class="p">,</span> <span class="n">max_relative_error</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">test_ignore_x</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">test_check_grad_ingore_x</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">check_grad</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">op</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;Y&quot;</span><span class="p">],</span>
<span class="s2">&quot;Out&quot;</span><span class="p">,</span>
<span class="n">max_relative_error</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
<span class="n">no_grad_set</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;X&quot;</span><span class="p">})</span>
<span class="p">[</span><span class="s1">&#39;Y&#39;</span><span class="p">],</span> <span class="s1">&#39;Out&#39;</span><span class="p">,</span> <span class="n">max_relative_error</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">no_grad_set</span><span class="o">=</span><span class="nb">set</span><span class="p">(</span><span class="s2">&quot;X&quot;</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">test_ignore_y</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">test_check_grad_ingore_y</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">check_grad</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">op</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;X&quot;</span><span class="p">],</span>
<span class="s2">&quot;Out&quot;</span><span class="p">,</span>
<span class="n">max_relative_error</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
<span class="n">no_grad_set</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;Y&quot;</span><span class="p">})</span>
<span class="p">[</span><span class="s1">&#39;X&#39;</span><span class="p">],</span> <span class="s1">&#39;Out&#39;</span><span class="p">,</span> <span class="n">max_relative_error</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">no_grad_set</span><span class="o">=</span><span class="nb">set</span><span class="p">(</span><span class="s1">&#39;Y&#39;</span><span class="p">))</span>
</pre></div>
</div>
<p>Some key points in the code above include:</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">create_op(&quot;mul&quot;)</span></code> creates the backward operator&#8217;s corresponding forward operator.</li>
<li><code class="docutils literal"><span class="pre">compare_grad</span></code> compares results between utilizing the CPU and the GPU.</li>
<li><code class="docutils literal"><span class="pre">test_normal</span></code> calls <code class="docutils literal"><span class="pre">check_grad</span></code> to validate scaling tests&#8217; correctness and stability through numeric methods.<ul>
<li>The first variable <code class="docutils literal"><span class="pre">self.op</span></code> denotes the forward operator.</li>
<li>The second variable <code class="docutils literal"><span class="pre">self.inputs</span></code> denotes the input dictionary, which has its key value identical to its <code class="docutils literal"><span class="pre">ProtoMaker</span></code> definitions.</li>
<li>The third variable <code class="docutils literal"><span class="pre">[&quot;X&quot;,</span> <span class="pre">&quot;Y&quot;]</span></code> appoints <code class="docutils literal"><span class="pre">X</span></code> and <code class="docutils literal"><span class="pre">Y</span></code> to be scale tested.</li>
<li>The fourth variable <code class="docutils literal"><span class="pre">&quot;Out&quot;</span></code> points to the network&#8217;s final output target <code class="docutils literal"><span class="pre">Out</span></code>.</li>
<li>The first variable <code class="docutils literal"><span class="pre">[&quot;X&quot;,</span> <span class="pre">&quot;Y&quot;]</span></code> appoints <code class="docutils literal"><span class="pre">X</span></code> and <code class="docutils literal"><span class="pre">Y</span></code> to be scale tested.</li>
<li>The second variable <code class="docutils literal"><span class="pre">&quot;Out&quot;</span></code> points to the network&#8217;s final output target <code class="docutils literal"><span class="pre">Out</span></code>.</li>
<li>The third variable <code class="docutils literal"><span class="pre">max_relative_error</span></code> points to the maximum relative tolerance error during scaling tests.</li>
</ul>
</li>
<li><code class="docutils literal"><span class="pre">test_ignore_x</span></code> and <code class="docutils literal"><span class="pre">test_ignore_y</span></code>branches test the cases where there is only one scaling input.</li>
<li><code class="docutils literal"><span class="pre">test_check_grad_ingore_x</span></code> and <code class="docutils literal"><span class="pre">test_check_grad_ingore_y</span></code>branches test the cases where there is only one scaling input.</li>
</ul>
</div>
<div class="section" id="compiling-and-running">
......
develop/doc/searchindex.js
浏览文件 @ 20cb5f06
因为 它太大了无法显示 source diff 。你可以改为 查看blob。
develop/doc_cn/_sources/howto/dev/new_op_cn.md.txt
浏览文件 @ 20cb5f06
......@@ -285,41 +285,27 @@ class TestMulGradOp(GradientChecker):
'Y': np.random.random((84, 100)).astype("float32")
}
def test_cpu_gpu_compare(self):
self.compare_grad(self.op, self.inputs)
def test_normal(self):
def test_check_grad_normal(self):
# mul op will enlarge the relative error
self.check_grad(
self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5)
self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5)
def test_ignore_x(self):
def test_check_grad_ingore_x(self):
self.check_grad(
self.op,
self.inputs, ["Y"],
"Out",
max_relative_error=0.5,
no_grad_set={"X"})
['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X"))
def test_ignore_y(self):
def test_check_grad_ingore_y(self):
self.check_grad(
self.op,
self.inputs, ["X"],
"Out",
max_relative_error=0.5,
no_grad_set={"Y"})
['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
```
下面解释代码中一些关键的地方:
- 调用`create_op("mul")`创建反向Op对应的前向Op。
- 调用`compare_grad`函数对比CPU、GPU计算结果。
- `test_normal`中调用`check_grad`使用数值法检测梯度正确性和稳定性。
- 第一个参数`self.op` : 前向Op。
- 第二个参数`self.inputs` : 输入词典，词典的Key和`ProtoMaker`定义保持一致。
- 第三个参数`["X", "Y"]` : 指定对输入变量`X`、`Y`做梯度检测。
- 第四个参数`"Out"` : 指定前向网络最终的输出目标变量`Out`
- `test_ignore_x`和`test_ignore_y`分支用来测试只需要计算一个输入梯度的情况。
- `test_check_grad_normal`中调用`check_grad`使用数值法检测梯度正确性和稳定性。
- 第一个参数`["X", "Y"]` : 指定对输入变量`X`、`Y`做梯度检测。
- 第二个参数`"Out"` : 指定前向网络最终的输出目标变量`Out`。
- 第三个参数`max_relative_error`：指定检测梯度时能容忍的最大错误值。
- `test_check_grad_ingore_x`和`test_check_grad_ingore_y`分支用来测试只需要计算一个输入梯度的情况。
### 编译和执行单元测试
......
develop/doc_cn/howto/dev/new_op_cn.html
浏览文件 @ 20cb5f06
......@@ -452,43 +452,29 @@ Kernel实现 | CPU、GPU共享Kernel实现在<code class="docutils literal
<span class="s1">&#39;Y&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="mi">84</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;float32&quot;</span><span class="p">)</span>
<span class="p">}</span>
<span class="k">def</span> <span class="nf">test_cpu_gpu_compare</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">compare_grad</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">op</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">test_normal</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">test_check_grad_normal</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="c1"># mul op will enlarge the relative error</span>
<span class="bp">self</span><span class="o">.</span><span class="n">check_grad</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">op</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;X&quot;</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">],</span> <span class="s2">&quot;Out&quot;</span><span class="p">,</span> <span class="n">max_relative_error</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">check_grad</span><span class="p">([</span><span class="s1">&#39;X&#39;</span><span class="p">,</span> <span class="s1">&#39;Y&#39;</span><span class="p">],</span> <span class="s1">&#39;Out&#39;</span><span class="p">,</span> <span class="n">max_relative_error</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">test_ignore_x</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">test_check_grad_ingore_x</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">check_grad</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">op</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;Y&quot;</span><span class="p">],</span>
<span class="s2">&quot;Out&quot;</span><span class="p">,</span>
<span class="n">max_relative_error</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
<span class="n">no_grad_set</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;X&quot;</span><span class="p">})</span>
<span class="p">[</span><span class="s1">&#39;Y&#39;</span><span class="p">],</span> <span class="s1">&#39;Out&#39;</span><span class="p">,</span> <span class="n">max_relative_error</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">no_grad_set</span><span class="o">=</span><span class="nb">set</span><span class="p">(</span><span class="s2">&quot;X&quot;</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">test_ignore_y</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">test_check_grad_ingore_y</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">check_grad</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">op</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;X&quot;</span><span class="p">],</span>
<span class="s2">&quot;Out&quot;</span><span class="p">,</span>
<span class="n">max_relative_error</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
<span class="n">no_grad_set</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;Y&quot;</span><span class="p">})</span>
<span class="p">[</span><span class="s1">&#39;X&#39;</span><span class="p">],</span> <span class="s1">&#39;Out&#39;</span><span class="p">,</span> <span class="n">max_relative_error</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">no_grad_set</span><span class="o">=</span><span class="nb">set</span><span class="p">(</span><span class="s1">&#39;Y&#39;</span><span class="p">))</span>
</pre></div>
</div>
<p>下面解释代码中一些关键的地方:</p>
<ul class="simple">
<li>调用<code class="docutils literal"><span class="pre">create_op(&quot;mul&quot;)</span></code>创建反向Op对应的前向Op。</li>
<li>调用<code class="docutils literal"><span class="pre">compare_grad</span></code>函数对比CPU、GPU计算结果。</li>
<li><code class="docutils literal"><span class="pre">test_normal</span></code>中调用<code class="docutils literal"><span class="pre">check_grad</span></code>使用数值法检测梯度正确性和稳定性。<ul>
<li>第一个参数<code class="docutils literal"><span class="pre">self.op</span></code> : 前向Op。</li>
<li>第二个参数<code class="docutils literal"><span class="pre">self.inputs</span></code> : 输入词典，词典的Key和<code class="docutils literal"><span class="pre">ProtoMaker</span></code>定义保持一致。</li>
<li>第三个参数<code class="docutils literal"><span class="pre">[&quot;X&quot;,</span> <span class="pre">&quot;Y&quot;]</span></code> : 指定对输入变量<code class="docutils literal"><span class="pre">X</span></code>、<code class="docutils literal"><span class="pre">Y</span></code>做梯度检测。</li>
<li>第四个参数<code class="docutils literal"><span class="pre">&quot;Out&quot;</span></code> : 指定前向网络最终的输出目标变量<code class="docutils literal"><span class="pre">Out</span></code></li>
<li><code class="docutils literal"><span class="pre">test_check_grad_normal</span></code>中调用<code class="docutils literal"><span class="pre">check_grad</span></code>使用数值法检测梯度正确性和稳定性。<ul>
<li>第一个参数<code class="docutils literal"><span class="pre">[&quot;X&quot;,</span> <span class="pre">&quot;Y&quot;]</span></code> : 指定对输入变量<code class="docutils literal"><span class="pre">X</span></code>、<code class="docutils literal"><span class="pre">Y</span></code>做梯度检测。</li>
<li>第二个参数<code class="docutils literal"><span class="pre">&quot;Out&quot;</span></code> : 指定前向网络最终的输出目标变量<code class="docutils literal"><span class="pre">Out</span></code>。</li>
<li>第三个参数<code class="docutils literal"><span class="pre">max_relative_error</span></code>：指定检测梯度时能容忍的最大错误值。</li>
</ul>
</li>
<li><code class="docutils literal"><span class="pre">test_ignore_x</span></code>和<code class="docutils literal"><span class="pre">test_ignore_y</span></code>分支用来测试只需要计算一个输入梯度的情况。</li>
<li><code class="docutils literal"><span class="pre">test_check_grad_ingore_x</span></code>和<code class="docutils literal"><span class="pre">test_check_grad_ingore_y</span></code>分支用来测试只需要计算一个输入梯度的情况。</li>
</ul>
</div>
<div class="section" id="">
......
develop/doc_cn/searchindex.js
浏览文件 @ 20cb5f06
此差异已折叠。
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或