SINGA -475 add Sub operator implementation to singa

python/singa/autograd.py

@@ -1828,3 +1828,21 @@ def backward(self, dy):
 
 def leakyrelu(x, a=0.01):
     return LeakyRelu(a)(x)[0]
+
+
+class Sub(Operation):
+    def __init__(self):
+        super(Sub, self)._init_()    
+
+    def forward(self, a, b):    
+        if training:
+            self.input = (a, b)
+            return singa.__sub__(a, b)
+
+    def backward(self, dy):
+        return dy, singa.MultFloat(dy, -1.0)
+
+
+def sub(a, b):
+    return Sub()(a,b)[0]
+

test/python/test_operation.py

@@ -322,6 +322,21 @@ def test_LeakyRelu(self):
         np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT)
         self.check_shape(dx.shape(), (3, 2))
 
+    def test_Sub(self):
+        X=np.array([0.8,-1.2,3.3,-3.6,-0.5,0.5]).reshape(3,2).astype(np.float32)
+        Y=np.array([4.4,5.3,3.2,3.7,5.4,6.3]).reshape(3,2).astype(np.float32)
+        x = tensor.from_numpy(X)
+        y = tensor.from_numpy(Y)
+        x.to_device(gpu_dev) 
+        y.to_device(gpu_dev) 
+
+        result=autograd.sub(x,y)
+        dx=result.creator.backward(x.data)[0]  
+
+        result_np=np.subtract(X,Y)      
+        np.testing.assert_array_almost_equal(tensor.to_numpy(result), result_np)
+        self.check_shape(dx.shape(), (3, 2)) 
+
     def test_Cos_cpu(self):
         X = np.array([0.8, -1.2, 3.3, -3.6, -0.5, 0.5]).reshape(3, 2).astype(np.float32)
         XT = np.cos(X)