optimizer adapt IndexedSlices

mindspore/nn/optim/adam.py

@@ -108,24 +108,26 @@ def _check_learning_rate_value(learning_rate, end_learning_rate, decay_steps, po
     validator.check_integer('decay_steps', decay_steps, 0, Rel.GT, prim_name)
 
 
-@_adam_opt.register("Function", "Function", "Tensor", "Tensor", "Tensor", "Tensor", "Number", "Tensor", "Tuple",
+@_adam_opt.register("Function", "Function", "Tensor", "Tensor", "Tensor", "Tensor", "Number", "Tensor", "IndexedSlices",
                     "Tensor", "Tensor", "Tensor", "Bool")
 def _run_opt_with_sparse(opt, sparse_opt, beta1_power, beta2_power, beta1, beta2, eps, lr, gradient, params,
                          moment1, moment2, ps_parameter):
     """Apply sparse adam optimizer to the weight parameter when the gradient is sparse."""
     success = True
+    indices = gradient.indices()
+    values = gradient.values()
     if ps_parameter:
         op_shape = P.Shape()
         _ps_pull = P.Pull()
         _ps_push = P.Push("Adam", [0, 1, 2])
         shapes = (op_shape(params), op_shape(moment1), op_shape(moment2),
                   op_shape(beta1_power), op_shape(beta2_power), op_shape(lr), op_shape(beta1),
-                  op_shape(beta2), op_shape(eps), op_shape(gradient[1]), op_shape(gradient[0]))
+                  op_shape(beta2), op_shape(eps), op_shape(values), op_shape(indices))
         success = F.depend(success, _ps_pull(_ps_push((beta1_power, beta2_power, lr, beta1, beta2,
-                                                       eps, gradient[1], gradient[0]), shapes), params))
+                                                       eps, values, indices), shapes), params))
     else:
         success = F.depend(success, sparse_opt(params, moment1, moment2, beta1_power, beta2_power, lr, beta1, beta2,
-                                               eps, gradient[1], gradient[0]))
+                                               eps, values, indices))
     return success
 
 
@@ -149,17 +151,19 @@ def _run_opt_with_one_number(opt, sparse_opt, beta1_power, beta2_power, beta1, b
 
 
 @_adam_push_pull_opt.register("Function", "Function", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
-                              "Tensor", "Tuple", "Tensor", "Tensor", "Tensor")
+                              "Tensor", "IndexedSlices", "Tensor", "Tensor", "Tensor")
 def _run_push_pull_opt_with_sparse(push, pull, beta1_power, beta2_power, beta1, beta2, eps, lr, gradient, params,
                                    moment1, moment2):
     """Apply sparse adam optimizer by push and pull to the weight parameter when the gradient is sparse."""
     success = True
     op_shape = P.Shape()
+    values = gradient.values()
+    indices = gradient.indices()
     shapes = (op_shape(params), op_shape(moment1), op_shape(moment2),
               op_shape(beta1_power), op_shape(beta2_power), op_shape(lr), op_shape(beta1),
-              op_shape(beta2), op_shape(eps), op_shape(gradient[1]), op_shape(gradient[0]))
+              op_shape(beta2), op_shape(eps), op_shape(values), op_shape(indices))
     success = F.depend(success, pull(push((beta1_power, beta2_power, lr, beta1, beta2,
-                                           eps, gradient[1], gradient[0]), shapes), params))
+                                           eps, values, indices), shapes), params))
     return success
 
 

mindspore/nn/optim/ftrl.py

@@ -25,20 +25,22 @@ _ftrl_opt = C.MultitypeFuncGraph("ftrl_opt")
 _ftrl_push_pull_opt = C.MultitypeFuncGraph("ftrl_opt")
 
 
-@_ftrl_opt.register("Function", "Function", "Tensor", "Number", "Number", "Number", "Tensor", "Tuple", "Tensor",
+@_ftrl_opt.register("Function", "Function", "Tensor", "Number", "Number", "Number", "Tensor", "IndexedSlices", "Tensor",
                     "Tensor", "Bool")
 def _tensor_run_opt_with_sparse(opt, spars_opt, learning_rate, l1, l2, lr_power, linear, gradient, weight, moment,
                                 ps_parameter):
     """Apply sparse ftrl optimizer to the weight parameter when the gradient is sparse."""
     success = True
+    indices = gradient.indices()
+    values = gradient.values()
     if ps_parameter:
         op_shape = P.Shape()
         _ps_pull = P.Pull()
         _ps_push = P.Push("Ftrl", [0, 1, 2])
-        shapes = (op_shape(weight), op_shape(moment), op_shape(linear), op_shape(gradient[1]), op_shape(gradient[0]))
-        success = F.depend(success, _ps_pull(_ps_push((gradient[1], gradient[0]), shapes), weight))
+        shapes = (op_shape(weight), op_shape(moment), op_shape(linear), op_shape(values), op_shape(indices))
+        success = F.depend(success, _ps_pull(_ps_push((values, indices), shapes), weight))
     else:
-        success = F.depend(success, spars_opt(weight, moment, linear, gradient[1], gradient[0]))
+        success = F.depend(success, spars_opt(weight, moment, linear, values, indices))
     return success
 
 
@@ -58,14 +60,16 @@ def _tensor_run_opt(opt, spars_opt, learning_rate, l1, l2, lr_power, linear, gra
     return success
 
 
-@_ftrl_push_pull_opt.register("Function", "Function", "Tensor", "Number", "Number", "Number", "Tensor", "Tuple",
+@_ftrl_push_pull_opt.register("Function", "Function", "Tensor", "Number", "Number", "Number", "Tensor", "IndexedSlices",
                               "Tensor", "Tensor")
 def _tensor_run_push_pull_opt_with_sparse(push, pull, learning_rate, l1, l2, lr_power, linear, gradient,
                                           weight, moment):
     success = True
     op_shape = P.Shape()
-    shapes = (op_shape(weight), op_shape(moment), op_shape(linear), op_shape(gradient[1]), op_shape(gradient[0]))
-    success = F.depend(success, pull(push((gradient[1], gradient[0]), shapes), weight))
+    values = gradient.values()
+    indices = gradient.indices()
+    shapes = (op_shape(weight), op_shape(moment), op_shape(linear), op_shape(values), op_shape(indices))
+    success = F.depend(success, pull(push((values, indices), shapes), weight))
     return success
 
 

mindspore/nn/optim/lazyadam.py

@@ -27,14 +27,14 @@ from .optimizer import Optimizer
 _lazy_adam_opt = C.MultitypeFuncGraph("lazy_adam_opt")
 
 
-@_lazy_adam_opt.register("Function", "Function", "Tensor", "Tensor", "Tensor", "Tensor", "Number", "Tensor", "Tuple",
-                         "Tensor", "Tensor", "Tensor")
+@_lazy_adam_opt.register("Function", "Function", "Tensor", "Tensor", "Tensor", "Tensor", "Number", "Tensor",
+                         "IndexedSlices", "Tensor", "Tensor", "Tensor")
 def _run_opt_with_sparse(opt, sparse_opt, beta1_power, beta2_power, beta1, beta2, eps, lr, gradient, params,
                          moment1, moment2):
     """Apply sparse lazy adam optimizer to the weight parameter when the gradient is sparse."""
     success = True
     success = F.depend(success, sparse_opt(params, moment1, moment2, beta1_power, beta2_power, lr, beta1, beta2,
-                                           eps, gradient[1], gradient[0]))
+                                           eps, gradient.values(), gradient.indices()))
     return success
 
 

mindspore/nn/optim/optimizer.py

@@ -22,7 +22,7 @@ from mindspore.ops import functional as F, composite as C, operations as P
 from mindspore.nn.cell import Cell
 from mindspore.common.parameter import Parameter, ParameterTuple
 from mindspore.common.initializer import initializer
-from mindspore.common.tensor import Tensor
+from mindspore.common.tensor import Tensor, IndexedSlices
 import mindspore.common.dtype as mstype
 from mindspore._checkparam import Validator as validator
 from mindspore._checkparam import Rel
@@ -490,12 +490,14 @@ op_gather = P.GatherV2()
 _apply_decay = C.MultitypeFuncGraph("apply_decay")
 
 
-@_apply_decay.register("Number", "Bool", "Tensor", "Tuple")
+@_apply_decay.register("Number", "Bool", "Tensor", "IndexedSlices")
 def _tensor_apply_decay_with_sparse(weight_decay, if_apply, weight, gradient):
     """Get grad with weight_decay."""
     if if_apply:
-        weight = op_gather(weight, gradient[0], 0)
-        return gradient[0], op_add((weight * weight_decay, gradient[1])), gradient[2]
+        indices = gradient.indices()
+        values = op_add((op_gather(weight, indices, 0) * weight_decay, gradient.values()))
+        shape = gradient.dense_shape()
+        return IndexedSlices(indices, values, shape)
     return gradient
 
 
@@ -518,9 +520,9 @@ def tensor_grad_scale(scale, grad):
     return grad * scale
 
 
-@_grad_scale.register("Number", "Tuple")
+@_grad_scale.register("Number", "IndexedSlices")
 def tensor_grad_scale_with_sparse(scale, grad):
     """Get grad with scale."""
     if scale == 1.0:
         return grad
-    return grad[0], grad[1] * scale, grad[2]
+    return IndexedSlices(grad.indices(), grad.values() * scale, grad.dense_shape())

mindspore/nn/optim/proximal_ada_grad.py

@@ -23,11 +23,12 @@ from .optimizer import Optimizer
 
 _proximal_ada_grad_opt = C.MultitypeFuncGraph("proximal_ada_grad_opt")
 
-@_proximal_ada_grad_opt.register("Function", "Function", "Tensor", "Tensor", "Tensor", "Tuple", "Tensor", "Tensor")
+@_proximal_ada_grad_opt.register("Function", "Function", "Tensor", "Tensor", "Tensor", "IndexedSlices", "Tensor",
+                                 "Tensor")
 def _tensor_run_opt_with_sparse(opt, sparse_opt, learning_rate, l1, l2, gradient, weight, accum):
     """Apply sparse proximal_ada_grad optimizer to the weight parameter."""
     success = True
-    success = F.depend(success, sparse_opt(weight, accum, learning_rate, l1, l2, gradient[1], gradient[0]))
+    success = F.depend(success, sparse_opt(weight, accum, learning_rate, l1, l2, gradient.values(), gradient.indices()))
     return success
 
 

mindspore/nn/wrap/grad_reducer.py

@@ -16,6 +16,7 @@
 from mindspore import context
 from mindspore.nn.cell import Cell
 from mindspore.communication.management import GlobalComm, get_group_size
+from mindspore.common.tensor import IndexedSlices
 from mindspore.ops import functional as F, composite as C, operations as P
 from mindspore.ops.operations.comm_ops import AllReduce, AllGather
 from mindspore.parallel._auto_parallel_context import auto_parallel_context
@@ -77,7 +78,7 @@ def _tensors_allreduce(degree, mean, allgather, allreduce_filter, grad, allreduc
     return grad
 
 
-@reduce_opt.register("Number", "Bool", "Function", "Bool", "Tuple", "Function")
+@reduce_opt.register("Number", "Bool", "Function", "Bool", "IndexedSlices", "Function")
 def _tensors_allreduce_with_sparse(degree, mean, allgather, allreduce_filter, grad, allreduce):
     """
     Apply allgather on gradient instead of allreduce for sparse feature.
@@ -88,21 +89,21 @@ def _tensors_allreduce_with_sparse(degree, mean, allgather, allreduce_filter, gr
         mean (bool): When mean is true, the mean coefficient (degree) would apply on gradients.
         allgather (Primitive): The communication operator for sparse gradients.
         allreduce_filter (bool): When it is true, allgather would apply.
-        grad (tuple): The indices, gradient tensor and tensor_shape before operation.
+        grad (IndexedSlices): The gradient before operation.
         allreduce (Primitive): The communication operator for gradients.
 
     Returns:
-        Tuple, include indices, the gradient tensor and tensor_shape after operation.
+        IndexedSlices, the gradient after operation.
     """
     if allreduce_filter:
-        indices = allgather(grad[0])
-        dout = allgather(grad[1])
+        indices = allgather(grad.indices())
+        dout = allgather(grad.values())
         if mean:
-            degree = F.scalar_cast(degree, F.dtype(grad[1]))
+            degree = F.scalar_cast(degree, F.dtype(grad.values()))
             cast_op = P.Cast()
             mul_op = P.Mul()
             dout = mul_op(dout, cast_op(F.scalar_to_array(1.0 / degree), F.dtype(dout)))
-        grad = (indices, dout, grad[2])
+        grad = IndexedSlices(indices, dout, grad.dense_shape())
     return grad
 
 
@@ -123,18 +124,18 @@ def _tensors_get_datatype(grad):
     return F.dtype(grad)
 
 
-@_get_datatype.register("Tuple")
+@_get_datatype.register("IndexedSlices")
 def _tensors_get_datatype_with_sparse(grad):
     """
     Acquire gradient datatype.
 
     Args:
-        grad (Tuple): The gradient tensor before operation.
+        grad (IndexedSlices): The gradient before operation.
 
     Returns:
         mstype, the datatype of gradient.
     """
-    return F.dtype(grad[1])
+    return F.dtype(grad.values())
 
 
 _cast_datatype = C.MultitypeFuncGraph("_cast_datatype")
@@ -155,20 +156,20 @@ def _tensors_cast_datatype(datatype, grad):
     return F.cast(grad, datatype)
 
 
-@_cast_datatype.register("TypeType", "Tuple")
+@_cast_datatype.register("TypeType", "IndexedSlices")
 def _tensors_cast_datatype_with_sparse(datatype, grad):
     """
     Cast gradient to datatype.
 
     Args:
         datatype (mstype): the destination datatype of gradient.
-        grad (Tuple): The gradient tensor before operation.
+        grad (IndexedSlices): The gradient before operation.
 
     Returns:
-        Tuple, the gradient tuple after operation.
+        IndexedSlices, the gradient after operation.
     """
-    dout = F.cast(grad[1], datatype)
-    return (grad[0], dout, grad[2])
+    dout = F.cast(grad.values(), datatype)
+    return IndexedSlices(grad.indices(), dout, grad.dense_shape())
 
 
 class DistributedGradReducer(Cell):

mindspore/ops/_grad/grad_array_ops.py

@@ -25,6 +25,7 @@ from .grad_base import bprop_getters
 from ..primitive import constexpr
 from ... import context
 from ...common import dtype as mstype
+from ...common.tensor import IndexedSlices
 
 reduce_sum = P.ReduceSum()
 unsorted_segment_sum = P.UnsortedSegmentSum()
@@ -206,7 +207,7 @@ def get_bprop_embedding_lookup(self):
         actual_dout_shape_changed = new_indices_shape_changed + x_shp_tail
         # Reshape the 'actual_dout' on device
         actual_dout = reshape_op(dout, actual_dout_shape_changed)
-        return (new_indices, actual_dout, x_shp), zeros_like(indices), zeros_like(offset)
+        return IndexedSlices(new_indices, actual_dout, x_shp), zeros_like(indices), zeros_like(offset)
     return bprop_sparse
 
 
@@ -335,7 +336,7 @@ def get_bprop_sparse_gather_v2(self):
             values_shape = indices_size + x_tail_shp
             values = reshape(dout, values_shape)
             indices = reshape(indices, indices_size)
-            return (indices, values, x_shp), zeros_like(indices), zeros_like(axis)
+            return IndexedSlices(indices, values, x_shp), zeros_like(indices), zeros_like(axis)
         if F.rank(dout) == 0:
             dout = P.ExpandDims()(dout, -1)
         if F.rank(indices) == 0:

mindspore/ops/_grad/grad_comm_ops.py

@@ -17,6 +17,7 @@
 import mindspore.common.dtype as mstype
 from mindspore.ops import functional as F
 from .. import operations as P
+from ...common.tensor import IndexedSlices
 from ..composite.multitype_ops.zeros_like_impl import zeros_like
 from ..operations.comm_ops import (AllGather, _HostAllGather, AllReduce, _AlltoAll, Broadcast,
                                    _GetTensorSlice, _MirrorOperator, ReduceOp,
@@ -46,9 +47,9 @@ def get_bprop_all_reduce(self):
             if F.issubclass_(F.typeof(dout), mstype.tensor):
                 dx = all_reduce_grad(dout)
             else:
-                indices = all_gather(dout[0])
-                grad = all_gather(dout[1])
-                dx = (indices, grad, dout[2])
+                indices = all_gather(dout.indices())
+                grad = all_gather(dout.values())
+                dx = IndexedSlices(indices, grad, dout.dense_shape())
             return (dx,)
     else:
 
@@ -59,12 +60,12 @@ def get_bprop_all_reduce(self):
                 z = cast(z, dtype(dx))
                 dx = mul(dx, z)
             else:
-                indices = all_gather(dout[0])
-                grad = all_gather(dout[1])
+                indices = all_gather(dout.indices())
+                grad = all_gather(dout.values())
                 z = equal(x, out)
                 z = cast(z, dtype(grad))
                 grad = mul(grad, z)
-                dx = (indices, grad, dout[2])
+                dx = IndexedSlices(indices, grad, dout.dense_shape())
             return (dx,)
     return bprop
 
@@ -194,19 +195,19 @@ def get_bprop_mirror_operator(self):
                 num = F.scalar_cast(dev_num, F.dtype(dx))
                 dx = mul(dx, cast(F.scalar_to_array(float_one/num), F.dtype(dx)))
             else:
-                indices = all_gather(dout[0])
-                grad = all_gather(dout[1])
+                indices = all_gather(dout.indices())
+                grad = all_gather(dout.values())
                 float_one = F.scalar_cast(1.0, F.dtype(grad))
                 num = F.scalar_cast(dev_num, F.dtype(grad))
                 grad = mul(grad, cast(F.scalar_to_array(float_one/num), F.dtype(grad)))
-                dx = (indices, grad, dout[2])
+                dx = (indices, grad, dout.dense_shape())
         else:
             if F.issubclass_(F.typeof(dout), mstype.tensor):
                 dx = all_reduce(dout)
             else:
-                indices = all_gather(dout[0])
-                grad = all_gather(dout[1])
-                dx = (indices, grad, dout[2])
+                indices = all_gather(dout.indices())
+                grad = all_gather(dout.values())
+                dx = (indices, grad, dout.dense_shape())
 
         return (dx,)
     return bprop

tests/ut/python/nn/optim/test_adam_with_tuple_grad.py

-# 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.
-# ============================================================================
-""" test adam """
-import numpy as np
-
-import mindspore.nn as nn
-from mindspore import Tensor, Parameter, context
-from mindspore.common.api import _executor
-from mindspore.common import dtype as mstype
-from mindspore.nn import TrainOneStepCell, WithLossCell
-from mindspore.nn.optim import Optimizer
-from mindspore.ops import operations as P
-from mindspore.ops import composite as C
-from mindspore.ops import functional as F
-from mindspore._checkparam import Validator as validator
-from mindspore._checkparam import Rel
-
-context.set_context(enable_sparse=True)
-
-adam_opt_for_map = C.MultitypeFuncGraph("adam_opt_for_map")
-@adam_opt_for_map.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
-                           "Tensor", "Tensor", "Tensor", "Bool")
-def _update_run_op_for_map(beta1, beta2, eps, lr, weight_decay_tensor, param, m, v, gradient, decay_flag):
-    op_mul = P.Mul()
-    op_square = P.Square()
-    op_sqrt = P.Sqrt()
-    op_cast = P.Cast()
-    op_reshape = P.Reshape()
-    op_shape = P.Shape()
-
-    param_fp32 = op_cast(param, mstype.float32)
-    m_fp32 = op_cast(m, mstype.float32)
-    v_fp32 = op_cast(v, mstype.float32)
-    gradient_fp32 = op_cast(gradient, mstype.float32)
-
-    next_m = op_mul(beta1, m_fp32) + op_mul(op_cast(F.tuple_to_array((1.0,)), mstype.float32) - beta1, gradient_fp32)
-
-    next_v = op_mul(beta2, v_fp32) + op_mul(op_cast(F.tuple_to_array((1.0,)), mstype.float32)
-                                            - beta2, op_square(gradient_fp32))
-
-    update = next_m / (op_sqrt(next_v) + eps)
-    if decay_flag:
-        update = update + op_mul(weight_decay_tensor, param_fp32)
-
-    update_with_lr = op_mul(lr, update)
-    next_param = param_fp32 - op_reshape(update_with_lr, op_shape(param_fp32))
-
-    next_v = F.depend(next_v, F.assign(param, next_param))
-    next_v = F.depend(next_v, F.assign(m, next_m))
-    next_v = F.depend(next_v, F.assign(v, next_v))
-    return next_v
-
-
-@adam_opt_for_map.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
-                           "Tensor", "Tensor", "Tuple", "Bool")
-def _update_run_op_sparse_for_map(beta1, beta2, eps, lr, weight_decay_tensor, param, m, v, gradient, decay_flag):
-    return gradient[2][2]
-
-def _check_param_value(beta1, beta2, eps, weight_decay, prim_name):
-    """Check the type of inputs."""
-    validator.check_value_type("beta1", beta1, [float], prim_name)
-    validator.check_value_type("beta2", beta2, [float], prim_name)
-    validator.check_value_type("eps", eps, [float], prim_name)
-    validator.check_value_type("weight_dacay", weight_decay, [float], prim_name)
-    validator.check_number_range("beta1", beta1, 0.0, 1.0, Rel.INC_NEITHER, prim_name)
-    validator.check_number_range("beta2", beta2, 0.0, 1.0, Rel.INC_NEITHER, prim_name)
-    validator.check_number_range("eps", eps, 0.0, float("inf"), Rel.INC_NEITHER, prim_name)
-    validator.check_number_range("weight_decay", weight_decay, 0.0, float("inf"), Rel.INC_LEFT, prim_name)
-
-
-class AdamWeightDecaySparse(Optimizer):
-    """
-    Implements Adam algorithm weight decay fix.
-
-    Args:
-        params (list[Parameter]): A list of parameter, which will be updated. The element in `params`
-                                  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. Default: 1e-3.
-        beta1 (float): The exponential decay rate for the 1st moment estimates. Default: 0.9.
-            Should be in range (0.0, 1.0).
-        beta2 (float): The exponential decay rate for the 2nd moment estimates. Default: 0.999.
-            Should be in range (0.0, 1.0).
-        eps (float): Term added to the denominator to improve numerical stability. Default: 1e-6.
-            Should be greater than 0.
-        weight_decay (float): Weight decay (L2 penalty). Default: 0.0.
-        decay_filter (Function): A function to determine whether to apply weight decay on parameters. Default:
-                                 lambda x: 'LayerNorm' not in x.name and 'bias' not in x.name.
-
-    Inputs:
-        - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`,
-          and might be in sparse format.
-
-    Outputs:
-        tuple[Parameter], the updated velocity value, the shape is the same as `params`.
-
-    Examples:
-        >>> net = Net()
-        >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-        >>> optim = nn.AdamWeightDecay(params=net.trainable_params())
-        >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
-   """
-    def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-6, weight_decay=0.0,
-                 decay_filter=lambda x: 'beta' not in x.name and 'gamma' not in x.name):
-        super(AdamWeightDecaySparse, self).__init__(learning_rate, params)
-        if self.is_group:
-            raise RuntimeError(f"The {self.cls_name} optimizer cannot support group setting.")
-        _check_param_value(beta1, beta2, eps, weight_decay, self.cls_name)
-        self.beta1 = Tensor(np.array([beta1]).astype(np.float32))
-        self.beta2 = Tensor(np.array([beta2]).astype(np.float32))
-        self.eps = Tensor(np.array([eps]).astype(np.float32))
-        self.weight_decay_tensor = Tensor(np.array([weight_decay]).astype(np.float32))
-
-        self.params = self.parameters
-        self.moments1 = self.params.clone(prefix="adam_m", init='zeros')
-        self.moments2 = self.params.clone(prefix="adam_v", init='zeros')
-        self.decay_flag = tuple(decay_filter(x) for x in self.params)
-
-        self.map = C.Map()
-
-    def construct(self, gradients):
-        lr = self.get_lr()
-        updated_velocity = self.map(F.partial(adam_opt_for_map, self.beta1, self.beta2, self.eps, lr,
-                                              self.weight_decay_tensor),
-                                    self.params, self.moments1, self.moments2, gradients, self.decay_flag)
-
-        return updated_velocity
-
-
-def test_AdamWeightDecaySparse():
-    """ test_AdamWeightDecaySparse """
-    context.set_context(mode=context.GRAPH_MODE)
-    class Loss(nn.Cell):
-        def __init__(self):
-            super(Loss, self).__init__()
-        def construct(self, base, target):
-            return base
-    class NetWithSparseGatherV2(nn.Cell):
-        def __init__(self):
-            super(NetWithSparseGatherV2, self).__init__()
-            self.w1 = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="w1")
-            self.w2 = Parameter(Tensor(np.ones([2, 1, 2]).astype(np.float32)), name="w2")
-            self.gatherv2 = P.SparseGatherV2()
-            self.axis = 0
-        def construct(self, indices):
-            return self.gatherv2(self.w1, indices, self.axis) * self.w2
-
-    inputs = Tensor(np.array([0, 1]).astype(np.int32))
-    label = Tensor(np.zeros([2, 1, 2]).astype(np.float32))
-    net = NetWithSparseGatherV2()
-    net.set_train()
-    loss = Loss()
-    optimizer = AdamWeightDecaySparse(net.trainable_params())
-
-    net_with_loss = WithLossCell(net, loss)
-    train_network = TrainOneStepCell(net_with_loss, optimizer)
-    _executor.compile(train_network, inputs, label)

tests/ut/python/parallel/test_sparse_feature_bprop.py

@@ -19,8 +19,8 @@ import mindspore as ms
 import mindspore.nn as nn
 from mindspore import context
 from mindspore.common import dtype as mstype
-from mindspore.common.tensor import Tensor
-from mindspore.ops import composite as C
+from mindspore.common.tensor import Tensor, IndexedSlices
+from mindspore.ops import composite as C, operations as P
 from mindspore.ops.operations.comm_ops import AllReduce, _MirrorOperator
 from mindspore.ops._grad.grad_base import bprop_getters
 from mindspore._checkparam import Validator as validator
@@ -65,7 +65,7 @@ def get_bprop_gather_v2(self):
     """Generate bprop for GatherV2"""
 
     def bprop(x, indices, axis, out, dout):
-        return (indices, dout, x), axis, out
+        return IndexedSlices(indices, dout, x), axis, out
 
     return bprop
 
@@ -78,7 +78,7 @@ def test_bprop_with_sparse_feature_allreduce():
             if shape is None:
                 shape = [8, 8]
             self.all_reduce = AllReduce()
-            self.gatherv2 = VirtualGatherV2()
+            self.gatherv2 = P.GatherV2()
             self.index = Tensor(np.ones(shape), dtype=ms.int32)
             self.axis = axis
 
@@ -102,7 +102,7 @@ def test_bprop_with_sparse_feature_mirror():
             if shape is None:
                 shape = [8, 8]
             self.mirror = _MirrorOperator(group=HCCL_WORLD_COMM_GROUP)
-            self.gatherv2 = VirtualGatherV2()
+            self.gatherv2 = P.GatherV2()
             self.index = Tensor(np.ones(shape), dtype=ms.int32)
             self.axis = axis