!21983 revert remove_redundant_depend in some network scripts

Merge pull request !21983 from huangbingjian/revert_depend

model_zoo/official/cv/psenet/src/network_define.py

@@ -23,6 +23,7 @@ from mindspore import ParameterTuple
 from mindspore.common.tensor import Tensor
 from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
 from mindspore.ops import composite as C
+from mindspore.ops import functional as F
 from mindspore.train.callback import Callback
 
 __all__ = ['LossCallBack', 'WithLossCell', 'TrainOneStepCell']
@@ -143,5 +144,4 @@ class TrainOneStepCell(nn.Cell):
         grads = self.grad(self.network, weights)(img, gt_text, gt_kernels, training_mask, self.sens)
         if self.reducer_flag:
             grads = self.grad_reducer(grads)
-        self.optimizer(grads)
+        return F.depend(loss, self.optimizer(grads))
-        return loss

model_zoo/official/cv/yolov3_resnet18/src/yolov3.py

@@ -678,8 +678,7 @@ class TrainingWrapper(nn.Cell):
         if self.reducer_flag:
             # apply grad reducer on grads
             grads = self.grad_reducer(grads)
-        self.optimizer(grads)
+        return F.depend(loss, self.optimizer(grads))
-        return loss
 
 
 class YoloBoxScores(nn.Cell):

model_zoo/official/gnn/gat/src/utils.py

@@ -18,6 +18,7 @@ from mindspore.common.parameter import ParameterTuple
 from mindspore import Tensor
 from mindspore.common import dtype as mstype
 from mindspore.ops import composite as C
+from mindspore.ops import functional as F
 from mindspore.ops import operations as P
 
 
@@ -149,8 +150,7 @@ class TrainOneStepCell(nn.Cell):
         loss = self.network(feature, biases)
         sens = P.Fill()(P.DType()(loss), P.Shape()(loss), self.sens)
         grads = self.grad(self.network, weights)(feature, biases, sens)
-        self.optimizer(grads)
+        return F.depend(loss, self.optimizer(grads))
-        return loss
 
 
 class TrainGAT(nn.Cell):

model_zoo/official/nlp/fasttext/src/fasttext_train.py

@@ -137,6 +137,4 @@ class FastTextTrainOneStepCell(nn.Cell):
         if self.reducer_flag:
             # apply grad reducer on grads
             grads = self.grad_reducer(grads)
-
+        return F.depend(loss, self.optimizer(grads))
-        self.optimizer(grads)
-        return loss

model_zoo/research/recommend/autodis/src/autodis.py

@@ -19,6 +19,7 @@ import numpy as np
 from sklearn.metrics import roc_auc_score
 import mindspore.common.dtype as mstype
 from mindspore.ops import composite as C
+from mindspore.ops import functional as F
 from mindspore.ops import operations as P
 from mindspore.nn import Dropout
 from mindspore.nn.optim import Adam
@@ -332,8 +333,7 @@ class TrainStepWrap(nn.Cell):
         loss = self.network(batch_ids, batch_wts, label)
         sens = P.Fill()(P.DType()(loss), P.Shape()(loss), self.sens) #
         grads = self.grad(self.network, weights)(batch_ids, batch_wts, label, sens)
-        self.optimizer(grads)
+        return F.depend(loss, self.optimizer(grads))
-        return loss
 
 
 class PredictWithSigmoid(nn.Cell):