!758 remove name arg from gradoperation

Merge pull request !758 from riemann_penn/remove_name_arg_from_gradoperation

tutorials/notebook/debugging_in_pynative_mode.ipynb

@@ -373,7 +373,7 @@
     "\n",
     "    def construct(self, x, label):\n",
     "        weights = self.weights\n",
-    "        return C.GradOperation('get_by_list', get_by_list=True)(self.network, weights)(x, label)"
+    "        return C.GradOperation(get_by_list=True)(self.network, weights)(x, label)"
    ]
   },
   {

tutorials/notebook/linear_regression.ipynb

@@ -524,7 +524,7 @@
     "\n",
     "    def construct(self, data, label):\n",
     "        weights = self.weights\n",
-    "        return C.GradOperation('get_by_list', get_by_list=True) \\\n",
+    "        return C.GradOperation(get_by_list=True) \\\n",
     "            (self.network, weights)(data, label)\n"
    ]
   },

tutorials/source_en/advanced_use/debugging_in_pynative_mode.md

@@ -262,7 +262,7 @@ def mul(x, y):
     return x * y
 
 def mainf(x, y):
-    return C.GradOperation('get_all', get_all=True)(mul)(x, y)
+    return C.GradOperation(get_all=True)(mul)(x, y)
 
 print(mainf(Tensor(1, mstype.int32), Tensor(2, mstype.int32)))
 ```
@@ -357,7 +357,7 @@ class GradWrap(nn.Cell):
 
     def construct(self, x, label):
         weights = self.weights
-        return C.GradOperation('get_by_list', get_by_list=True)(self.network, weights)(x, label)
+        return C.GradOperation(get_by_list=True)(self.network, weights)(x, label)
 
 net = LeNet5()
 optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.1, 0.9)

tutorials/source_en/use/custom_operator.md

@@ -232,7 +232,7 @@ def test_grad_net():
     x = np.array([1.0, 4.0, 9.0]).astype(np.float32)
     sens = np.array([1.0, 1.0, 1.0]).astype(np.float32)
     square = Net()
-    grad = C.GradOperation('grad_with_sens', sens_param=True)
+    grad = C.GradOperation(sens_param=True)
     dx = grad(square)(Tensor(x), Tensor(sens))
     print("x: ", x)
     print("dx: ", dx)

tutorials/source_zh_cn/advanced_use/debugging_in_pynative_mode.md

@@ -264,7 +264,7 @@ def mul(x, y):
     return x * y
 
 def mainf(x, y):
-    return C.GradOperation('get_all', get_all=True)(mul)(x, y)
+    return C.GradOperation(get_all=True)(mul)(x, y)
 
 print(mainf(Tensor(1, mstype.int32), Tensor(2, mstype.int32)))
 ```
@@ -359,7 +359,7 @@ class GradWrap(nn.Cell):
 
     def construct(self, x, label):
         weights = self.weights
-        return C.GradOperation('get_by_list', get_by_list=True)(self.network, weights)(x, label)
+        return C.GradOperation(get_by_list=True)(self.network, weights)(x, label)
 
 net = LeNet5()
 optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.1, 0.9)

tutorials/source_zh_cn/advanced_use/gradient_accumulation.md

@@ -100,7 +100,7 @@ class TrainForwardBackward(Cell):
         self.weights = ParameterTuple(network.trainable_params())
         self.optimizer = optimizer
         self.grad_sum = grad_sum
-        self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
+        self.grad = C.GradOperation(get_by_list=True, sens_param=True)
         self.sens = sens
         self.hyper_map = C.HyperMap()
 

tutorials/source_zh_cn/quick_start/linear_regression.md

@@ -297,7 +297,7 @@ class GradWrap(nn.Cell):
 
     def construct(self, data, label):
         weights = self.weights
-        return C.GradOperation('get_by_list', get_by_list=True) \
+        return C.GradOperation(get_by_list=True) \
             (self.network, weights)(data, label)
 
 ```

tutorials/source_zh_cn/use/custom_operator.md

@@ -232,7 +232,7 @@ def test_grad_net():
     x = np.array([1.0, 4.0, 9.0]).astype(np.float32)
     sens = np.array([1.0, 1.0, 1.0]).astype(np.float32)
     square = Net()
-    grad = C.GradOperation('grad_with_sens', sens_param=True)
+    grad = C.GradOperation(sens_param=True)
     dx = grad(square)(Tensor(x), Tensor(sens))
     print("x: ", x)
     print("dx: ", dx)

tutorials/tutorial_code/gradient_accumulation/train.py

@@ -41,7 +41,7 @@ class TrainForwardBackward(Cell):
         self.weights = ParameterTuple(network.trainable_params())
         self.optimizer = optimizer
         self.grad_sum = grad_sum
-        self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
+        self.grad = C.GradOperation(get_by_list=True, sens_param=True)
         self.sens = sens
         self.hyper_map = C.HyperMap()
 

tutorials/tutorial_code/linear_regression.py

@@ -41,7 +41,7 @@ class GradWrap(nn.Cell):
 
     def construct(self, data, label):
         weights = self.weights
-        return C.GradOperation('get_by_list', get_by_list=True) \
+        return C.GradOperation(get_by_list=True) \
             (self.network, weights)(data, label)
 
 # Initializing model functions