adapt SparseApplyAdagradD adding output accum

graphengine

@@ -1 +1 @@
-Subproject commit 43a715bc461fd70b7837051a2f47f0a1b19c5859
+Subproject commit d345a800a4f7c32eb768ea48667d1ce00b841748

mindspore/ccsrc/transform/op_declare.cc

@@ -1133,7 +1133,7 @@ INPUT_MAP(SparseApplyAdagradD) = {
   {1, INPUT_DESC(var)}, {2, INPUT_DESC(accum)}, {3, INPUT_DESC(grad)}, {4, INPUT_DESC(indices)}};
 ATTR_MAP(SparseApplyAdagradD) = {{"lr", ATTR_DESC(lr, AnyTraits<float>())},
                                  {"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())}};
-OUTPUT_MAP(SparseApplyAdagradD) = {{0, OUTPUT_DESC(var)}};
+OUTPUT_MAP(SparseApplyAdagradD) = {{0, OUTPUT_DESC(var)}, {1, OUTPUT_DESC(accum)}};
 
 // SparseApplyFtrlD
 INPUT_MAP(SparseApplyFtrlD) = {{1, INPUT_DESC(var)},

mindspore/ops/operations/nn_ops.py

@@ -2433,7 +2433,10 @@ class SparseApplyAdagrad(PrimitiveWithInfer):
           The shape of `indices` must be the same as `grad` in first dimension, the type must be int32.
 
     Outputs:
-        Tensor, has the same shape and type as `var`.
+        Tuple of 2 Tensor, the updated parameters.
+
+        - **var** (Tensor) - The same shape and data type as `var`.
+        - **accum** (Tensor) - The same shape and data type as `accum`.
     """
 
     @prim_attr_register
@@ -2448,13 +2451,13 @@ class SparseApplyAdagrad(PrimitiveWithInfer):
             validator.check('var_shape[1:]', var_shape[1:], 'grad_shape[1:]', grad_shape[1:], Rel.EQ, self.name)
         validator.check_integer("indices rank", len(indices_shape), 1, Rel.EQ, self.name)
         validator.check('grad_shape[0]', grad_shape[0], 'indices_shape[0]', indices_shape[0], Rel.EQ, self.name)
-        return var_shape
+        return var_shape, accum_shape
 
     def infer_dtype(self, var_type, accum_type, grad_type, indices_type):
         args = {'var': var_type, 'accum': accum_type, 'grad': grad_type}
         validator.check_tensor_type_same(args, (mstype.float32,), self.name)
         validator.check_tensor_type_same({'indices': indices_type}, [mstype.int32], self.name)
-        return var_type
+        return var_type, accum_type
 
 
 class LARSUpdate(PrimitiveWithInfer):

tests/ut/python/ops/test_ops.py

@@ -814,7 +814,7 @@ test_case_nn_ops = [
     ('SparseApplyAdagrad', {
         'block': P.SparseApplyAdagrad(0.5),
         'desc_inputs': [[3, 3], [3, 3], [3, 3], Tensor(np.ones((3,), np.int32))],
-        'desc_bprop': [3, 3],
+        'desc_bprop': [[3, 3], [3, 3]],
         'skip': ['backward']}),
     ('Flatten_1', {
         'block': NetForFlatten(),