remove ControlDepend from ternarybert

mindspore/ops/operations/other_ops.py

@@ -423,9 +423,9 @@ class Depend(Primitive):
     In order to ensure that operator A is executed before operator B, it is recommended to
     insert the Depend operator between operators A and B. The usage method is as follows::
 
-        out_a = A(in_a)
-        in_b = Depend(in_b, out_a)
-        out_b = B(in_b)
+        a = A(x)                --->        a = A(x)
+        b = B(y)                --->        y = Depend(y, a)
+                                --->        b = B(y)
 
     Inputs:
         - **value** (Tensor) - the real value to return for depend operator.

model_zoo/research/nlp/ternarybert/src/cell_wrapper.py

@@ -377,21 +377,16 @@ class BertTrainWithLossScaleCell(nn.Cell):
                   sens=None):
         """Defines the computation performed."""
         weights = self.weights
-        saved = ()
         for i in range(self.length):
-            saved = saved + (F.assign(self.saved_params[i], weights[i]),)
+            F.assign(self.saved_params[i], weights[i])
 
         for i in range(self.quant_embedding_list_length):
             quant_embedding = self.quantize_embedding(weights[self.quant_embedding_list[i]])
-            quant_embedding = F.depend(quant_embedding, saved)
-            assign_embedding = F.assign(weights[self.quant_embedding_list[i]], quant_embedding)
-            input_ids = F.depend(input_ids, assign_embedding)
+            F.assign(weights[self.quant_embedding_list[i]], quant_embedding)
 
         for i in range(self.quant_weight_list_length):
             quant_weight = self.quantize_weight(weights[self.quant_weight_list[i]])
-            quant_weight = F.depend(quant_weight, saved)
-            assign_weight = F.assign(weights[self.quant_weight_list[i]], quant_weight)
-            input_ids = F.depend(input_ids, assign_weight)
+            F.assign(weights[self.quant_weight_list[i]], quant_weight)
 
         if sens is None:
             scaling_sens = self.loss_scale
@@ -411,10 +406,10 @@ class BertTrainWithLossScaleCell(nn.Cell):
         grads = self.grad_reducer(grads)
         grads = self.hyper_map(F.partial(grad_scale, scaling_sens * self.degree), grads)
         grads = self.hyper_map(F.partial(clip_grad, self.clip_type, self.clip_value), grads)
-        restore = ()
+
         for i in range(self.length):
-            weights[i] = F.depend(weights[i], grads)
-            restore = restore + (F.assign(weights[i], self.saved_params[i]),)
+            param = F.depend(self.saved_params[i], grads)
+            F.assign(weights[i], param)
 
         self.get_status(init)
         flag_sum = self.reduce_sum(init, (0,))
@@ -431,7 +426,6 @@ class BertTrainWithLossScaleCell(nn.Cell):
             succ = False
         else:
             succ = self.optimizer(grads)
-        succ = F.depend(succ, restore)
         return succ
 
 
@@ -490,21 +484,16 @@ class BertTrainCell(nn.Cell):
                   label_ids):
         """Defines the computation performed."""
         weights = self.weights
-        saved = ()
         for i in range(self.length):
-            saved = saved + (F.assign(self.saved_params[i], weights[i]),)
+            F.assign(self.saved_params[i], weights[i])
 
         for i in range(self.quant_embedding_list_length):
             quant_embedding = self.quantize_embedding(weights[self.quant_embedding_list[i]])
-            quant_embedding = F.depend(quant_embedding, saved)
-            assign_embedding = F.assign(weights[self.quant_embedding_list[i]], quant_embedding)
-            input_ids = F.depend(input_ids, assign_embedding)
+            F.assign(weights[self.quant_embedding_list[i]], quant_embedding)
 
         for i in range(self.quant_weight_list_length):
             quant_weight = self.quantize_weight(weights[self.quant_weight_list[i]])
-            quant_weight = F.depend(quant_weight, saved)
-            assign_weight = F.assign(weights[self.quant_weight_list[i]], quant_weight)
-            input_ids = F.depend(input_ids, assign_weight)
+            F.assign(weights[self.quant_weight_list[i]], quant_weight)
 
         grads = self.grad(self.network, weights)(input_ids,
                                                  input_mask,
@@ -515,11 +504,10 @@ class BertTrainCell(nn.Cell):
         # apply grad reducer on grads
         grads = self.grad_reducer(grads)
         grads = self.hyper_map(F.partial(clip_grad, self.clip_type, self.clip_value), grads)
-        restore = ()
+
         for i in range(self.length):
-            weights[i] = F.depend(weights[i], grads)
-            restore = restore + (F.assign(weights[i], self.saved_params[i]),)
+            param = F.depend(self.saved_params[i], grads)
+            F.assign(weights[i], param)
 
         succ = self.optimizer(grads)
-        succ = F.depend(succ, restore)
         return succ