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develop/doc_cn/_sources/howto/dev/new_op_cn.md.txt

@@ -286,28 +286,50 @@ class TestMulOp(unittest.TestCase):
 
 反向Op单测继承自`GradientChecker`，而`GradientChecker`集成自`unittest.TestCase`，所以反向单测函数需要`test_`开头。
 
- ```
- class MulGradOpTest(GradientChecker):
-    def test_mul(self):
-        op = create_op("mul")
-        inputs = {
+```
+class TestMulGradOp(GradientChecker):
+    def setUp(self):
+        self.op = create_op("mul")
+        self.inputs = {
             'X': np.random.random((32, 84)).astype("float32"),
             'Y': np.random.random((84, 100)).astype("float32")
         }
-        self.compare_grad(op, inputs)      
+
+    def test_cpu_gpu_compare(self):
+        self.compare_grad(self.op, self.inputs)
+
+    def test_normal(self):
         # mul op will enlarge the relative error
         self.check_grad(
-            op, inputs, set(["X", "Y"]), "Out", max_relative_error=0.5)
- ```
+            self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5)
+
+    def test_ignore_x(self):
+        self.check_grad(
+            self.op,
+            self.inputs, ["Y"],
+            "Out",
+            max_relative_error=0.5,
+            no_grad_set={"X"})
+
+    def test_ignore_y(self):
+        self.check_grad(
+            self.op,
+            self.inputs, ["X"],
+            "Out",
+            max_relative_error=0.5,
+            no_grad_set={"Y"})
+```
+
+下面解释一些关键的地方:
 
    - 调用`create_op("mul")`创建反向Op对应的前向Op。
-   - 定义输入`inputs`。
    - 调用`compare_grad`函数对比CPU、GPU计算结果。
-   - 调用`check_grad`检查梯度稳定性，这里采用数值法检测梯度正确性。
-      - 第一个参数`op` : 前向op。
-      - 第二个参数`inputs` : 输入词典，词典的Key和`ProtoMaker`定义保持一致。
-      - 第三个参数`set(["X", "Y"])` : 指定对输入变量`X`、`Y`做梯度检测。
+   - `test_normal`中调用`check_grad`检查梯度稳定性，这里采用数值法检测梯度正确性。
+      - 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`分支测试只需要计算一个输入梯度的情况。
 
 
 ### 编译和执行 

develop/doc_cn/howto/dev/new_op_cn.html

@@ -439,30 +439,51 @@ Kernel实现       | CPU、GPU共享Kernel在<code class="docutils literal"><spa
 <div class="section" id="operator">
 <span id="id7"></span><h3>反向Operator单测<a class="headerlink" href="#operator" title="永久链接至标题">¶</a></h3>
 <p>反向Op单测继承自<code class="docutils literal"><span class="pre">GradientChecker</span></code>，而<code class="docutils literal"><span class="pre">GradientChecker</span></code>集成自<code class="docutils literal"><span class="pre">unittest.TestCase</span></code>，所以反向单测函数需要<code class="docutils literal"><span class="pre">test_</span></code>开头。</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MulGradOpTest</span><span class="p">(</span><span class="n">GradientChecker</span><span class="p">):</span>
-   <span class="k">def</span> <span class="nf">test_mul</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-       <span class="n">op</span> <span class="o">=</span> <span class="n">create_op</span><span class="p">(</span><span class="s2">&quot;mul&quot;</span><span class="p">)</span>
-       <span class="n">inputs</span> <span class="o">=</span> <span class="p">{</span>
-           <span class="s1">&#39;X&#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">32</span><span class="p">,</span> <span class="mi">84</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="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="bp">self</span><span class="o">.</span><span class="n">compare_grad</span><span class="p">(</span><span class="n">op</span><span class="p">,</span> <span class="n">inputs</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="n">op</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="nb">set</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>
+<div class="highlight-default"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">TestMulGradOp</span><span class="p">(</span><span class="n">GradientChecker</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">setUp</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">op</span> <span class="o">=</span> <span class="n">create_op</span><span class="p">(</span><span class="s2">&quot;mul&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s1">&#39;X&#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">32</span><span class="p">,</span> <span class="mi">84</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="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="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="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="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="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="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>
 </pre></div>
 </div>
+<p>下面解释一些关键的地方:</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>定义输入<code class="docutils literal"><span class="pre">inputs</span></code>。</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>调用<code class="docutils literal"><span class="pre">check_grad</span></code>检查梯度稳定性，这里采用数值法检测梯度正确性。<ul>
-<li>第一个参数<code class="docutils literal"><span class="pre">op</span></code> : 前向op。</li>
-<li>第二个参数<code class="docutils literal"><span class="pre">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">set([&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><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>检查梯度稳定性，这里采用数值法检测梯度正确性。<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>
 </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>分支测试只需要计算一个输入梯度的情况。</li>
 </ul>
 </div>
 <div class="section" id="">