add RMSProp optimizer

mindspore/ccsrc/transform/convert.cc

@@ -184,6 +184,8 @@ const char kNameDiagPart[] = "DiagPart";
 const char kNameSpaceToBatch[] = "SpaceToBatch";
 const char kNameBatchToSpace[] = "BatchToSpace";
 const char kNameAtan2[] = "Atan2";
+const char kNameApplyRMSProp[] = "ApplyRMSProp";
+const char kNameApplyCenteredRMSProp[] = "ApplyCenteredRMSProp";
 
 // -----------------OpAdapter initialization--------------
 std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_map() {
@@ -369,7 +371,9 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {string(kNameDiagPart), ADPT_DESC(DiagPart)},
     {string(kNameSpaceToBatch), ADPT_DESC(SpaceToBatchD)},
     {string(kNameBatchToSpace), ADPT_DESC(BatchToSpaceD)},
-    {string(kNameAtan2), ADPT_DESC(Atan2)}};
+    {string(kNameAtan2), ADPT_DESC(Atan2)},
+    {string(kNameApplyRMSProp), ADPT_DESC(ApplyRMSPropD)},
+    {string(kNameApplyCenteredRMSProp), ADPT_DESC(ApplyCenteredRMSProp)}};
 #ifdef ENABLE_GE
   adpt_map[string(kNamePrint)] = ADPT_DESC(Print);
 #endif

mindspore/ccsrc/transform/op_declare.cc

@@ -1189,6 +1189,22 @@ INPUT_MAP(Atan2) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}};
 ATTR_MAP(Atan2) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(Atan2) = {{0, OUTPUT_DESC(y)}};
 
+// ApplyRMSPropD
+INPUT_MAP(ApplyRMSPropD) = {
+  {1, INPUT_DESC(var)}, {2, INPUT_DESC(ms)}, {3, INPUT_DESC(mom)}, {4, INPUT_DESC(grad)}, {5, INPUT_DESC(lr)}};
+INPUT_ATTR_MAP(ApplyRMSPropD) = {{6, ATTR_DESC(rho, AnyTraits<float>())},
+                                 {7, ATTR_DESC(momentum, AnyTraits<float>())},
+                                 {8, ATTR_DESC(epsilon, AnyTraits<float>())}};
+ATTR_MAP(ApplyRMSPropD) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())}};
+OUTPUT_MAP(ApplyRMSPropD) = {{0, OUTPUT_DESC(var)}};
+
+// ApplyCenteredRMSProp
+INPUT_MAP(ApplyCenteredRMSProp) = {{1, INPUT_DESC(var)}, {2, INPUT_DESC(mg)},       {3, INPUT_DESC(ms)},
+                                   {4, INPUT_DESC(mom)}, {5, INPUT_DESC(grad)},     {6, INPUT_DESC(lr)},
+                                   {7, INPUT_DESC(rho)}, {8, INPUT_DESC(momentum)}, {9, INPUT_DESC(epsilon)}};
+ATTR_MAP(ApplyCenteredRMSProp) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())}};
+OUTPUT_MAP(ApplyCenteredRMSProp) = {{0, OUTPUT_DESC(var)}};
+
 #ifdef ENABLE_GE
 // Print
 INPUT_MAP(Print) = EMPTY_INPUT_MAP;

mindspore/ccsrc/transform/op_declare.h

@@ -447,6 +447,12 @@ DECLARE_OP_ADAPTER(BatchToSpaceD)
 DECLARE_OP_USE_OUTPUT(BatchToSpaceD)
 DECLARE_OP_ADAPTER(Atan2)
 DECLARE_OP_USE_OUTPUT(Atan2)
+DECLARE_OP_ADAPTER(ApplyRMSPropD)
+DECLARE_OP_USE_INPUT_ATTR(ApplyRMSPropD)
+DECLARE_OP_USE_OUTPUT(ApplyRMSPropD)
+DECLARE_OP_ADAPTER(ApplyCenteredRMSProp)
+DECLARE_OP_USE_OUTPUT(ApplyCenteredRMSProp)
+
 #ifdef ENABLE_GE
 DECLARE_OP_ADAPTER(Print)
 DECLARE_OP_USE_DYN_INPUT(Print)

mindspore/nn/optim/__init__.py

@@ -25,6 +25,7 @@ from .lamb import Lamb
 from .sgd import SGD
 from .lars import LARS
 from .ftrl import FTRL
+from .rmsprop import RMSProp
 
 __all__ = ['Optimizer', 'Momentum', 'LARS', 'Adam', 'AdamWeightDecay',
-           'AdamWeightDecayDynamicLR', 'Lamb', 'SGD', 'FTRL']
+           'AdamWeightDecayDynamicLR', 'Lamb', 'SGD', 'FTRL', 'RMSProp']

mindspore/nn/optim/rmsprop.py

@@ -0,0 +1,187 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""rmsprop"""
+from mindspore.ops import functional as F, composite as C, operations as P
+from mindspore.common.initializer import initializer
+from mindspore.common.parameter import Parameter
+from mindspore._checkparam import ParamValidator as validator
+import mindspore.common.dtype as mstype
+from .optimizer import Optimizer, grad_scale
+
+rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt")
+centered_rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt")
+
+
+@rmsprop_opt.register("Function", "Number", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor", "Tensor")
+def _rmsprop_opt(opt, learning_rate, decay, epsilon, momentum, weight, ms, mom, grad):
+    """Apply rmsprop optimizer to the weight parameter."""
+    success = True
+    success = F.depend(success, opt(weight, ms, mom, grad, learning_rate, decay, momentum, epsilon))
+    return success
+
+
+@rmsprop_opt.register("Function", "Tensor", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor", "Tensor")
+def _rmsprop_opt_dynamic_lr(opt, learning_rate, decay, epsilon, momentum, weight, ms, mom, grad):
+    """Apply rmsprop optimizer to the weight parameter using dynamic learning rate."""
+    success = True
+    success = F.depend(success, opt(weight, ms, mom, grad, learning_rate, decay, momentum, epsilon))
+    return success
+
+
+@centered_rmsprop_opt.register("Function", "Number", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor",
+                               "Tensor", "Tensor")
+def _centered_rmsprop_opt(opt, learning_rate, decay, epsilon, momentum, weight, mg, ms, mom, grad):
+    """Apply centered rmsprop optimizer to the weight parameter."""
+    success = True
+    success = F.depend(success, opt(weight, mg, ms, mom, grad, learning_rate, decay, momentum, epsilon))
+    return success
+
+
+@centered_rmsprop_opt.register("Function", "Tensor", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor",
+                               "Tensor", "Tensor")
+def _centered_rmsprop_opt_dynamic_lr(opt, learning_rate, decay, epsilon, momentum, weight, mg, ms, mom, grad):
+    """Apply centered rmsprop optimizer to the weight parameter using dynamic learning rate."""
+    success = True
+    success = F.depend(success, opt(weight, mg, ms, mom, grad, learning_rate, decay, momentum, epsilon))
+    return success
+
+
+class RMSProp(Optimizer):
+    """
+    Implements Root Mean Squared Propagation (RMSProp) algorithm.
+
+    Note:
+        Update `params` according to the RMSProp algorithm.
+
+        The equation is as follows:
+
+        ..  math::
+            s_{t} = \\rho s_{t-1} + (1 - \\rho)(\\nabla Q_{i}(w))^2
+
+        ..  math::
+            m_{t} = \\beta m_{t-1} + \\frac{\\eta} {\\sqrt{s_{t} + \\epsilon}} \\nabla Q_{i}(w)
+
+        ..  math::
+            w = w - m_{t}
+
+        The first equation calculates moving average of the squared gradient for
+        each weight. Then dividing the gradient by :math:`\\sqrt{ms_{t} + \\epsilon}`.
+
+        if centered is True:
+
+        ..  math::
+            g_{t} = \\rho g_{t-1} + (1 - \\rho)\\nabla Q_{i}(w)
+
+        ..  math::
+            s_{t} = \\rho s_{t-1} + (1 - \\rho)(\\nabla Q_{i}(w))^2
+
+        ..  math::
+            m_{t} = \\beta m_{t-1} + \\frac{\\eta} {\\sqrt{s_{t} - g_{t}^2 + \\epsilon}} \\nabla Q_{i}(w)
+
+        ..  math::
+            w = w - m_{t}
+
+        where, :math:`w` represents `params`, which will be updated.
+        :math:`g_{t}` is mean gradients, :math:`g_{t-1}` is the last moment of :math:`g_{t}`.
+        :math:`s_{t}` is the mean square gradients, :math:`s_{t-1}` is the last moment of :math:`s_{t}`,
+        :math:`m_{t}` is moment, the delta of `w`, :math:`m_{t-1}` is the last moment of :math:`m_{t}`.
+        :math:`\\rho` represents `decay`. :math:`\\beta` is the momentum term, represents `momentum`.
+        :math:`\\epsilon` is a smoothing term to avoid division by zero, represents `epsilon`.
+        :math:`\\eta` is learning rate, represents `learning_rate`. :math:`\\nabla Q_{i}(w)` is gradientse,
+        represents `gradients`.
+
+    Args:
+        params (list[Parameter]): A list of parameter, which will be updated. The element in `parameters`
+                                  should be class mindspore.Parameter.
+        learning_rate (Union[float, Tensor, Iterable]): A value for the learning rate. When the learning_rate is
+                                                        Iterable or a Tensor and the dims of the Tensor is 1,
+                                                        use dynamic learning rate, then the i-th step will
+                                                        take the i-th value as the learning rate.
+                                                        When the learning_rate is float or learning_rate is a Tensor
+                                                        but the dims of the Tensor is 0, use fixed learning rate.
+                                                        Other cases are not supported.
+        decay (float): Decay rate.
+        momentum (float): Hyperparameter of type float, means momentum for the moving average.
+        epsilon (float): Term added to the denominator to improve numerical stability. Should be greater than 0.
+        use_locking (bool): Enable a lock to protect the update of variable and accumlation tensors. Default: False.
+        centered (bool): If True, gradients are normalized by the estimated variance of the gradient. Default: False
+        loss_scale (float): A floating point value for the loss scale. Default: 1.0.
+
+    Inputs:
+        - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`.
+
+    Outputs:
+        Tensor[bool], the value is True.
+
+    Examples:
+        >>> net = Net()
+        >>> loss = nn.SoftmaxCrossEntropyWithLogits()
+        >>> opt = RMSProp(params=net.trainable_params(), learning_rate=lr)
+        >>> model = Model(net, loss, opt)
+    """
+    def __init__(self, params, learning_rate=0.1, decay=0.9, momentum=0.0, epsilon=1e-10,
+                 use_locking=False, centered=False, loss_scale=1.0):
+        super(RMSProp, self).__init__(learning_rate, params)
+
+        if isinstance(momentum, float) and momentum < 0.0:
+            raise ValueError("momentum should be at least 0.0, but got momentum {}".format(momentum))
+
+        if decay < 0.0:
+            raise ValueError("decay should be at least 0.0, but got dampening {}".format(decay))
+        self.decay = decay
+        self.epsilon = epsilon
+
+        validator.check_type("use_locking", use_locking, [bool])
+        validator.check_type("centered", centered, [bool])
+        self.centered = centered
+        if centered:
+            self.opt = P.ApplyCenteredRMSProp(use_locking)
+            self.mg = self.parameters.clone(prefix="mean_grad", init='zeros')
+        else:
+            self.opt = P.ApplyRMSProp(use_locking)
+
+        self.dynamic_lr = False
+        if not isinstance(learning_rate, float):
+            self.dynamic_lr = True
+            self.gather = P.GatherV2()
+            self.assignadd = P.AssignAdd()
+            self.global_step = Parameter(initializer(0, [1], mstype.int32), name="global_step")
+            self.axis = 0
+
+        self.momentum = momentum
+
+        self.ms = self.parameters.clone(prefix="mean_square", init='zeros')
+        self.moment = self.parameters.clone(prefix="moment", init='zeros')
+        self.hyper_map = C.HyperMap()
+
+        self.decay = decay
+        self.reciprocal_scale = 1.0 / loss_scale
+
+    def construct(self, gradients):
+        params = self.parameters
+        if self.reciprocal_scale != 1.0:
+            gradients = self.hyper_map(F.partial(grad_scale, self.reciprocal_scale), gradients)
+        if self.dynamic_lr:
+            lr = self.gather(self.learning_rate, self.global_step, self.axis)
+            F.control_depend(lr, self.assignadd(self.global_step, self.one))
+        else:
+            lr = self.learning_rate
+        if self.centered:
+            success = self.hyper_map(F.partial(centered_rmsprop_opt, self.opt, lr, self.decay, self.epsilon,
+                                               self.momentum), params, self.mg, self.ms, self.moment, gradients)
+        else:
+            success = self.hyper_map(F.partial(rmsprop_opt, self.opt, lr, self.decay, self.epsilon,
+                                               self.momentum), params, self.ms, self.moment, gradients)
+        return success

mindspore/ops/_grad/grad_math_ops.py

@@ -394,8 +394,8 @@ def _split_shape_index(input_shape, axis):
         axis = tuple([axis])
     reduction_indices = tuple([(i + rank) % rank for i in axis])
     other_indices = tuple(set(range(rank)) - set(reduction_indices))
-    reduced_num = reduce(lambda x, y: x * y, [input_shape[i] for i in reduction_indices])
+    reduced_num = reduce(lambda x, y: x * y, [1] + [input_shape[i] for i in reduction_indices])
-    other_num = reduce(lambda x, y: x * y, [input_shape[i] for i in other_indices])
+    other_num = reduce(lambda x, y: x * y, [1] + [input_shape[i] for i in other_indices])
     perm = reduction_indices + other_indices
     return tuple([reduced_num, other_num]), perm
 

mindspore/ops/operations/__init__.py

@@ -65,7 +65,8 @@ from .nn_ops import (LSTM, SGD, Adam, ApplyMomentum, BatchNorm,
                      SmoothL1Loss, Softmax,
                      SoftmaxCrossEntropyWithLogits, ROIAlign,
                      SparseSoftmaxCrossEntropyWithLogits, Tanh,
-                     TopK, BinaryCrossEntropy, SparseApplyAdagrad, LARSUpdate, ApplyFtrl, SparseApplyFtrlD)
+                     TopK, BinaryCrossEntropy, SparseApplyAdagrad, LARSUpdate, ApplyFtrl, SparseApplyFtrlD,
+                     ApplyRMSProp, ApplyCenteredRMSProp)
 from .other_ops import Assign, IOU, BoundingBoxDecode, BoundingBoxEncode, CheckValid, MakeRefKey
 
 
@@ -229,6 +230,8 @@ __all__ = [
     "SpaceToBatch",
     "BatchToSpace",
     "Atan2",
+    "ApplyRMSProp",
+    "ApplyCenteredRMSProp"
 ]
 
 __all__.sort()

mindspore/ops/operations/nn_ops.py

@@ -1359,6 +1359,158 @@ class SGD(PrimitiveWithInfer):
         validator.check_typename("stat_dtype", stat_dtype, [mstype.float16, mstype.float32])
         return parameters_dtype
 
+class ApplyRMSProp(PrimitiveWithInfer):
+    """
+    Optimizer that implements the Root Mean Square prop(RMSProp) algorithm.
+
+    Note:
+        Update `var` according to the RMSProp algorithm.
+
+        ..  math::
+            s_{t} = \\rho s_{t-1} + (1 - \\rho)(\\nabla Q_{i}(w))^2
+
+        ..  math::
+            m_{t} = \\beta m_{t-1} + \\frac{\\eta} {\\sqrt{s_{t} + \\epsilon}} \\nabla Q_{i}(w)
+
+        ..  math::
+            w = w - m_{t}
+
+        where, :math:`w` represents `var`, which will be updated.
+        :math:`s_{t}` represents `mean_square`, :math:`s_{t-1}` is the last momentent of :math:`s_{t}`,
+        :math:`m_{t}` represents `moment`, :math:`m_{t-1}` is the last momentent of :math:`m_{t}`.
+        :math:`\\rho` represents `decay`. :math:`\\beta` is the momentum term, represents `momentum`.
+        :math:`\\epsilon` is a smoothing term to avoid division by zero, represents `epsilon`.
+        :math:`\\eta` represents `learning_rate`. :math:`\\nabla Q_{i}(w)` represents `grad`.
+
+    Args:
+        use_locking (bool): Enable a lock to protect the update of variable tensors. Default: False.
+
+    Inputs:
+        - **var** (Tensor) - Weights to be update.
+        - **mean_square** (Tensor) - Mean square gradients, must have the same type as `var`.
+        - **moment** (Tensor) - Delta of `var`, must have the same type as `var`.
+        - **grad** (Tensor) - Gradients, must have the same type as `var`.
+        - **learning_rate** (Union[Number, Tensor]) - Learning rate.
+        - **decay** (float) - Decay rate.
+        - **momentum** (float) - Momentum.
+        - **epsilon** (float) - Ridge term.
+
+    Outputs:
+        Tensor, parameters to be update.
+
+    Examples:
+        >>> net = Net()
+        >>> loss = nn.SoftmaxCrossEntropyWithLogits()
+        >>> opt = RMSProp(params=net.trainable_params(), learning_rate=learning_rate)
+        >>> model = Model(net, loss, opt)
+    """
+
+    @prim_attr_register
+    def __init__(self, use_locking=False):
+        self.use_locking = validator.check_type("use_locking", use_locking, [bool])
+
+    def infer_shape(self, var_shape, mean_square_shape, moment_shape, grad_shape, learning_rate_shape, decay_shape,
+                    momentum_shape, epsilon_shape):
+        validator.check_param_equal("var_shape", var_shape, "mean_square_shape", mean_square_shape)
+        validator.check_param_equal("var_shape", var_shape, "moment_shape", moment_shape)
+        validator.check_param_equal("var_shape", var_shape, "grad_shape", grad_shape)
+        return var_shape
+
+    def infer_dtype(self, var_dtype, mean_square_dtype, moment_dtype, grad_dtype, learning_rate_dtype, decay_dtype,
+                    momentum_dtype, epsilon_dtype):
+        validator.check_subclass("var_dtype", var_dtype, mstype.tensor)
+        validator.check_subclass("mean_square_dtype", mean_square_dtype, mstype.tensor)
+        validator.check_subclass("moment_dtype", moment_dtype, mstype.tensor)
+        validator.check_subclass("grad_dtype", moment_dtype, mstype.tensor)
+        args = {"var_dtype": var_dtype, "mean_square_dtype": mean_square_dtype, "moment_dtype": moment_dtype,
+                "grad_dtype": grad_dtype}
+        validator.check_type_same(args, mstype.number_type)
+
+        args = {"learning_rate_dtype": learning_rate_dtype, "decay_dtype": decay_dtype,
+                'momentum_dtype': momentum_dtype, "epsilon_dtype": epsilon_dtype}
+        validator.check_type_same(args, [mstype.float16, mstype.float32])
+        return var_dtype
+
+
+class ApplyCenteredRMSProp(PrimitiveWithInfer):
+    """
+    Optimizer that implements the centered RMSProp algorithm.
+
+    Note:
+        Update `var` according to the centered RMSProp algorithm.
+
+        ..  math::
+            g_{t} = \\rho g_{t-1} + (1 - \\rho)\\nabla Q_{i}(w)
+
+        ..  math::
+            s_{t} = \\rho s_{t-1} + (1 - \\rho)(\\nabla Q_{i}(w))^2
+
+        ..  math::
+            m_{t} = \\beta m_{t-1} + \\frac{\\eta} {\\sqrt{s_{t} - g_{t}^2 + \\epsilon}} \\nabla Q_{i}(w)
+
+        ..  math::
+            w = w - m_{t}
+
+        where, :math:`w` represents `var`, which will be updated.
+        :math:`g_{t}` represents `mean_gradient`, :math:`g_{t-1}` is the last momentent of :math:`g_{t}`.
+        :math:`s_{t}` represents `mean_square`, :math:`s_{t-1}` is the last momentent of :math:`s_{t}`,
+        :math:`m_{t}` represents `moment`, :math:`m_{t-1}` is the last momentent of :math:`m_{t}`.
+        :math:`\\rho` represents `decay`. :math:`\\beta` is the momentum term, represents `momentum`.
+        :math:`\\epsilon` is a smoothing term to avoid division by zero, represents `epsilon`.
+        :math:`\\eta` represents `learning_rate`. :math:`\\nabla Q_{i}(w)` represents `grad`.
+
+    Args:
+        use_locking (bool): Enable a lock to protect the update of variable tensors. Default: False.
+
+    Inputs:
+        - **var** (Tensor) - Weights to be update.
+        - **mean_gradient** (Tensor) - Mean gradients, must have the same type as `var`.
+        - **mean_square** (Tensor) - Mean square gradients, must have the same type as `var`.
+        - **moment** (Tensor) - Delta of `var`, must have the same type as `var`.
+        - **grad** (Tensor) - Gradients, must have the same type as `var`.
+        - **learning_rate** (Union[Number, Tensor]) - Learning rate.
+        - **decay** (float) - Decay rate.
+        - **momentum** (float) - Momentum.
+        - **epsilon** (float) - Ridge term.
+
+    Outputs:
+        Tensor, parameters to be update.
+
+    Examples:
+        >>> net = Net()
+        >>> loss = nn.SoftmaxCrossEntropyWithLogits()
+        >>> opt = RMSProp(params=net.trainable_params(), learning_rate=learning_rate, centered=True)
+        >>> model = Model(net, loss, opt)
+    """
+
+    @prim_attr_register
+    def __init__(self, use_locking=False):
+        self.use_locking = validator.check_type("use_locking", use_locking, [bool])
+
+    def infer_shape(self, var_shape, mean_gradient_shape, mean_square_shape, moment_shape, grad_shape,
+                    learning_rate_shape, decay_shape, momentum_shape, epsilon_shape):
+        validator.check_param_equal("var_shape", var_shape, "mean_gradient_shape", mean_gradient_shape)
+        validator.check_param_equal("var_shape", var_shape, "mean_square_shape", mean_square_shape)
+        validator.check_param_equal("var_shape", var_shape, "moment_shape", moment_shape)
+        validator.check_param_equal("var_shape", var_shape, "grad_shape", grad_shape)
+        return var_shape
+
+    def infer_dtype(self, var_dtype, mean_gradient_dtype, mean_square_dtype, moment_dtype, grad_dtype,
+                    learning_rate_dtype, rho_dtype, momentum_dtype, epsilon_dtype):
+        validator.check_subclass("var_dtype", var_dtype, mstype.tensor)
+        validator.check_subclass("mean_gradient_dtype", mean_gradient_dtype, mstype.tensor)
+        validator.check_subclass("mean_square_dtype", mean_square_dtype, mstype.tensor)
+        validator.check_subclass("moment_dtype", moment_dtype, mstype.tensor)
+        validator.check_subclass("grad_dtype", moment_dtype, mstype.tensor)
+        args = {"var_dtype": var_dtype, "mean_gradient_dtype": mean_gradient_dtype,
+                "mean_square_dtype": mean_square_dtype, "moment_dtype": moment_dtype, "grad_dtype": grad_dtype}
+        validator.check_type_same(args, mstype.number_type)
+
+        args = {"learning_rate_dtype": learning_rate_dtype, "rho_dtype": rho_dtype, 'momentum_dtype': momentum_dtype,
+                "epsilon_dtype": epsilon_dtype}
+        validator.check_type_same(args, [mstype.float16, mstype.float32])
+        return var_dtype
+
 
 class LayerNorm(Primitive):
     r"""

tests/mindspore_test_framework/utils/block_util.py

@@ -223,6 +223,10 @@ class InputOpNet(nn.Cell):
         x = self.op(x1, x2, x3, x4, x5, self.c1)
         return x
 
+    def construct5_c4(self, x1, x2, x3, x4, x5):
+        x = self.op(x1, x2, x3, x4, x5, self.c1, self.c2, self.c3, self.c4)
+        return x
+
 def gen_net(op, input_num, training=True, desc_const=(), const_first=False, add_fake_input=False):
     if isinstance(op, nn.Cell):
         return op

tests/ut/python/ops/test_ops.py

@@ -810,6 +810,18 @@ test_case_nn_ops = [
         'desc_inputs': [[3, 3], [3, 3], [3, 3], [3, 3]],
         'desc_bprop': [3, 3],
         'skip': ['backward']}),
+    ('ApplyRMSProp', {
+        'block': P.ApplyRMSProp(),
+        'desc_const': [0.9, 0.0, 1e-10, 0.001],
+        'desc_inputs': [[3, 3], [3, 3], [3, 3], [3, 3]],
+        'desc_bprop': [3, 3],
+        'skip': ['backward']}),
+    ('ApplyCenteredRMSProp', {
+        'block': P.ApplyCenteredRMSProp(),
+        'desc_const': [0.9, 0.0, 1e-10, 0.001],
+        'desc_inputs': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3]],
+        'desc_bprop': [3, 3],
+        'skip': ['backward']}),
 ]
 
 test_case_array_ops = [