enable optimizer parallel with broadcast

mindspore/ccsrc/parallel/context.cc

@@ -62,6 +62,7 @@ void ParallelContext::Reset() {
   enable_all_reduce_fusion_ = false;
   strategy_ckpt_load_file_ = "";
   strategy_ckpt_save_file_ = "";
+  enable_parallel_optimizer_ = false;
 }
 
 void ParallelContext::set_device_num(int32_t device_num) {

mindspore/ccsrc/parallel/context.h

@@ -100,6 +100,11 @@ class ParallelContext {
   void set_strategy_ckpt_save_file(const std::string &strategy_ckpt_save_file);
   std::string strategy_ckpt_save_file() const { return strategy_ckpt_save_file_; }
 
+  void set_enable_parallel_optimizer(bool enable_parallel_optimizer) {
+    enable_parallel_optimizer_ = enable_parallel_optimizer;
+  }
+  bool enable_parallel_optimizer() const { return enable_parallel_optimizer_; }
+
   void Reset();
 
  private:
@@ -123,6 +128,7 @@ class ParallelContext {
   std::map<std::string, std::vector<uint32_t>> all_reduce_fusion_split_sizes_;
   std::string strategy_ckpt_load_file_;
   std::string strategy_ckpt_save_file_;
+  bool enable_parallel_optimizer_;
 };
 
 void ParallelParameterContextInit(const FuncGraphPtr &func_graph);

mindspore/ccsrc/pipeline/init.cc

@@ -205,6 +205,10 @@ PYBIND11_MODULE(_c_expression, m) {
     .def("get_strategy_ckpt_save_file", &ParallelContext::strategy_ckpt_save_file, "Get strategy checkpoint save file.")
     .def("set_full_batch", &ParallelContext::set_full_batch, "Set whether load full batch on each device.")
     .def("get_full_batch", &ParallelContext::full_batch, "Get whether load full batch on each device.")
+    .def("set_enable_parallel_optimizer", &ParallelContext::set_enable_parallel_optimizer,
+         "Set enable/disable parallel optimizer.")
+    .def("get_enable_parallel_optimizer", &ParallelContext::enable_parallel_optimizer,
+         "Get enable/disable parallel optimizer.")
     .def("reset", &ParallelContext::Reset, "Reset auto parallel context.");
 
   (void)py::class_<CostModelContext, std::shared_ptr<CostModelContext>>(m, "CostModelContext")

mindspore/nn/optim/adam.py

@@ -29,8 +29,9 @@ from .optimizer import Optimizer
 _adam_opt = C.MultitypeFuncGraph("adam_opt")
 
 
-@_adam_opt.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Bool")
-def _update_run_op(beta1, beta2, eps, lr, weight_decay_tensor, param, m, v, gradient, decay_flag):
+@_adam_opt.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
+                    "Tensor", "Bool", "Bool")
+def _update_run_op(beta1, beta2, eps, lr, weight_decay_tensor, param, m, v, gradient, decay_flag, optim_filter):
     """
     Update parameters.
 
@@ -44,38 +45,44 @@ def _update_run_op(beta1, beta2, eps, lr, weight_decay_tensor, param, m, v, grad
         m (Tensor): m value of parameters.
         v (Tensor): v value of parameters.
         gradient (Tensor): Gradient of parameters.
+        decay_flag (bool): Applies weight decay or not.
+        optim_filter (bool): Applies parameter update or not.
 
     Returns:
         Tensor, the new value of v after updating.
     """
-    op_mul = P.Mul()
-    op_square = P.Square()
-    op_sqrt = P.Sqrt()
-    op_cast = P.Cast()
-    op_reshape = P.Reshape()
-    op_shape = P.Shape()
+    if optim_filter:
+        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)
+        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_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))
+        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 / (eps + op_sqrt(next_v))
-    if decay_flag:
-        update = op_mul(weight_decay_tensor, param_fp32) + update
 
-    update_with_lr = op_mul(lr, update)
-    next_param = param_fp32 - op_reshape(update_with_lr, op_shape(param_fp32))
+        update = next_m / (eps + op_sqrt(next_v))
+        if decay_flag:
+            update = op_mul(weight_decay_tensor, param_fp32) + update
 
-    next_v = F.depend(next_v, F.assign(param, op_cast(next_param, F.dtype(param))))
-    next_v = F.depend(next_v, F.assign(m, op_cast(next_m, F.dtype(m))))
-    next_v = F.depend(next_v, F.assign(v, op_cast(next_v, F.dtype(v))))
-    return next_v
+        update_with_lr = op_mul(lr, update)
+        next_param = param_fp32 - op_reshape(update_with_lr, op_shape(param_fp32))
+
+        next_param = F.depend(next_param, F.assign(param, op_cast(next_param, F.dtype(param))))
+        next_param = F.depend(next_param, F.assign(m, op_cast(next_m, F.dtype(m))))
+        next_param = F.depend(next_param, F.assign(v, op_cast(next_v, F.dtype(v))))
+        return next_param
+    return gradient
 
 
 def _check_param_value(beta1, beta2, eps, weight_decay, prim_name):
@@ -300,7 +307,7 @@ class AdamWeightDecay(Optimizer):
         - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`.
 
     Outputs:
-        tuple[Parameter], the updated velocity value, the shape is the same as `params`.
+        tuple[bool], all elements are True.
 
     Examples:
         >>> net = Net()
@@ -328,11 +335,13 @@ class AdamWeightDecay(Optimizer):
 
     def construct(self, gradients):
         lr = self.get_lr()
-        updated_velocity = self.hyper_map(F.partial(_adam_opt, self.beta1, self.beta2, self.eps, lr,
-                                                    self.weight_decay_tensor),
-                                          self.params, self.moments1, self.moments2, gradients, self.decay_flag)
-
-        return updated_velocity
+        optim_result = self.hyper_map(F.partial(_adam_opt, self.beta1, self.beta2, self.eps, lr,
+                                                self.weight_decay_tensor),
+                                      self.params, self.moments1, self.moments2, gradients,
+                                      self.decay_flag, self.optim_filter)
+        if self.use_parallel:
+            optim_result = self.broadcast_params(optim_result)
+        return optim_result
 
 
 class AdamWeightDecayDynamicLR(Optimizer):
@@ -363,7 +372,7 @@ class AdamWeightDecayDynamicLR(Optimizer):
         - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`.
 
     Outputs:
-        tuple[Parameter], the updated velocity value, the shape is the same as `params`.
+        tuple[bool], all elements are True.
 
     Examples:
         >>> net = Net()
@@ -424,12 +433,14 @@ class AdamWeightDecayDynamicLR(Optimizer):
             warmup_lr = self.start_learning_rate * warmup_percent
             is_warmup = self.cast(self.greater(self.warmup_steps, self.global_step), mstype.float32)
             lr = (self.one - is_warmup) * lr + is_warmup * warmup_lr
-        updated_velocity = self.hyper_map(F.partial(_adam_opt, self.beta1, self.beta2, self.eps, lr,
-                                                    self.weight_decay_tensor),
-                                          self.params, self.moments1, self.moments2, gradients, self.decay_flag)
-
+        optim_result = self.hyper_map(F.partial(_adam_opt, self.beta1, self.beta2, self.eps, lr,
+                                                self.weight_decay_tensor),
+                                      self.params, self.moments1, self.moments2, gradients,
+                                      self.decay_flag, self.optim_filter)
+        if self.use_parallel:
+            optim_result = self.broadcast_params(optim_result)
         added_global_step = self.global_step + self.one
         F.control_depend(lr, added_global_step)
         self.global_step = added_global_step
 
-        return updated_velocity
+        return optim_result

mindspore/nn/optim/lamb.py

@@ -32,11 +32,10 @@ num_one = Tensor(np.ones([1]), mstype.float32)
 
 _lamb_opt = C.MultitypeFuncGraph("lamb_opt")
 
-
-@_lamb_opt.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
-                    "Tensor", "Tensor", "Tensor", "Tensor", "Bool")
+@_lamb_opt.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
+                    "Tensor", "Bool", "Bool")
 def _update_run_op(beta1, beta2, eps, lr, weight_decay_tensor, global_step, param, m, v,
-                   gradient, decay_flag):
+                   gradient, decay_flag, optim_filter):
     """
     Update parameters.
 
@@ -52,66 +51,66 @@ def _update_run_op(beta1, beta2, eps, lr, weight_decay_tensor, global_step, para
         v (Tensor): v value of parameters.
         gradient (Tensor): Gradient of parameters.
         decay_flag (bool): Specifies whether param update with weight decay.
+        optim_filter(bool): Applies parameter update or not.
 
     Returns:
         Tensor, the new value of v after updating.
     """
-    op_mul = P.Mul()
-    op_sqrt = P.Sqrt()
-    op_rsqrt = P.Rsqrt()
-    op_square = P.Square()
-    op_cast = P.Cast()
-    op_reshape = P.Reshape()
-    op_shape = P.Shape()
-    op_pow = P.Pow()
-    op_norm = layer.Norm()
-    op_select = P.Select()
-    op_greater = P.Greater()
-    op_fill = P.Fill()
-    op_dtype = P.DType()
-
-    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(num_one,
-                                                    mstype.float32) - beta1, gradient_fp32)
-
-    next_v = op_mul(beta2, v_fp32) + op_mul(op_cast(num_one,
-                                                    mstype.float32) - beta2, op_square(gradient_fp32))
-
-    next_mm = next_m / (op_cast(num_one, mstype.float32)
-                        - op_pow(beta1, op_cast(global_step + num_one, mstype.float32)))
-    next_vv = next_v / (op_cast(num_one, mstype.float32) -
-                        op_pow(beta2, op_cast(global_step + num_one, mstype.float32)))
-    w_norm = op_norm(param_fp32)
-    g_norm = op_norm(gradient_fp32)
-
-    g_norm_hat = op_norm(op_mul(next_mm, op_rsqrt(
-        next_vv + eps)) + weight_decay_tensor * param_fp32)
-    zeros = F.zeros_like(w_norm)
-    ones = op_fill(op_dtype(w_norm), op_shape(w_norm), 1.0)
-    trust_ratio = op_select(
-        op_greater(w_norm, zeros),
-        op_select(op_greater(g_norm, zeros), w_norm / g_norm_hat, ones),
-        ones)
-    tens = op_fill(op_dtype(trust_ratio), op_shape(trust_ratio), 10.0)
-    trust_ratio = C.clip_by_value(trust_ratio, zeros, tens)
-    update = next_mm / (op_sqrt(next_vv) + eps)
-
-    if decay_flag:
-        update = update + op_mul(weight_decay_tensor, param_fp32)
-
-    update_with_lr = op_mul(op_mul(trust_ratio, 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
+    if optim_filter:
+        op_mul = P.Mul()
+        op_sqrt = P.Sqrt()
+        op_rsqrt = P.Rsqrt()
+        op_square = P.Square()
+        op_cast = P.Cast()
+        op_reshape = P.Reshape()
+        op_shape = P.Shape()
+        op_pow = P.Pow()
+        op_norm = layer.Norm()
+        op_select = P.Select()
+        op_greater = P.Greater()
+        op_fill = P.Fill()
+        op_dtype = P.DType()
+
+        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(num_one, mstype.float32) - beta1, gradient_fp32)
+
+        next_v = op_mul(beta2, v_fp32) + op_mul(op_cast(num_one, mstype.float32) - beta2, op_square(gradient_fp32))
+
+        next_mm = next_m / (op_cast(num_one, mstype.float32)
+                            - op_pow(beta1, op_cast(global_step + num_one, mstype.float32)))
+        next_vv = next_v / (op_cast(num_one, mstype.float32) -
+                            op_pow(beta2, op_cast(global_step + num_one, mstype.float32)))
+        w_norm = op_norm(param_fp32)
+        g_norm = op_norm(gradient_fp32)
+
+        g_norm_hat = op_norm(op_mul(next_mm, op_rsqrt(next_vv + eps)) + weight_decay_tensor * param_fp32)
+        zeros = F.zeros_like(w_norm)
+        ones = op_fill(op_dtype(w_norm), op_shape(w_norm), 1.0)
+        trust_ratio = op_select(
+            op_greater(w_norm, zeros),
+            op_select(op_greater(g_norm, zeros), w_norm / g_norm_hat, ones),
+            ones)
+        tens = op_fill(op_dtype(trust_ratio), op_shape(trust_ratio), 10.0)
+        trust_ratio = C.clip_by_value(trust_ratio, zeros, tens)
+        update = next_mm / (op_sqrt(next_vv) + eps)
+
+        if decay_flag:
+            update = update + op_mul(weight_decay_tensor, param_fp32)
+
+        update_with_lr = op_mul(op_mul(trust_ratio, lr), update)
+
+        next_param = param_fp32 - op_reshape(update_with_lr, op_shape(param_fp32))
+
+        next_param = F.depend(next_param, F.assign(param, next_param))
+        next_param = F.depend(next_param, F.assign(m, next_m))
+        next_param = F.depend(next_param, F.assign(v, next_v))
+
+        return next_param
+    return gradient
 
 
 lamb_opt_graph_kernel = C.MultitypeFuncGraph("lamb_opt_graph_kernel")
@@ -238,7 +237,7 @@ class Lamb(Optimizer):
         - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`.
 
     Outputs:
-        tuple[Parameter], the updated velocity value, the shape is the same as `params`.
+        tuple[bool], all elements are True.
 
     Examples:
         >>> net = Net()
@@ -311,18 +310,21 @@ class Lamb(Optimizer):
                 self.warmup_steps, self.global_step), mstype.float32)
             lr = (self.one - is_warmup) * lr + is_warmup * warmup_lr
         if self.enable_graph_kernel:
-            updated_velocity = self.hyper_map(F.partial(lamb_opt_graph_kernel,
-                                                        self.beta1, self.beta2, self.eps, lr,
-                                                        self.weight_decay_tensor, self.global_step),
-                                              self.params, self.moments1, self.moments2, gradients, self.decay_flag)
+            optim_result = self.hyper_map(F.partial(lamb_opt_graph_kernel,
+                                                    self.beta1, self.beta2, self.eps, lr,
+                                                    self.weight_decay_tensor, self.global_step),
+                                          self.params, self.moments1, self.moments2, gradients, self.decay_flag)
         else:
-            updated_velocity = self.hyper_map(F.partial(_lamb_opt,
-                                                        self.beta1, self.beta2, self.eps, lr,
-                                                        self.weight_decay_tensor, self.global_step),
-                                              self.params, self.moments1, self.moments2, gradients, self.decay_flag)
+            optim_result = self.hyper_map(F.partial(_lamb_opt,
+                                                    self.beta1, self.beta2, self.eps, lr,
+                                                    self.weight_decay_tensor, self.global_step),
+                                          self.params, self.moments1, self.moments2, gradients,
+                                          self.decay_flag, self.optim_filter)
+        if self.use_parallel:
+            optim_result = self.broadcast_params(optim_result)
 
         added_global_step = self.global_step + self.one
         F.control_depend(lr, added_global_step)
         self.global_step = added_global_step
 
-        return updated_velocity
+        return optim_result

mindspore/nn/optim/optimizer.py

@@ -22,11 +22,14 @@ 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
 import mindspore.common.dtype as mstype
 from mindspore._checkparam import Validator as validator
 from mindspore._checkparam import Rel
-from mindspore.common.tensor import Tensor
 from mindspore import log as logger
+from mindspore.parallel._utils import _get_global_rank, _get_device_num, _get_parallel_mode
+from mindspore.parallel._auto_parallel_context import auto_parallel_context
+from mindspore.train.parallel_utils import ParallelMode
 
 __all__ = ['Optimizer']
 
@@ -155,6 +158,27 @@ class Optimizer(Cell):
         self.param_length = len(self.parameters)
         self.map_ = C.Map()
 
+        use_parallel = auto_parallel_context().get_enable_parallel_optimizer()
+        self.use_parallel = use_parallel
+        if use_parallel:
+            if self.cls_name not in ["Lamb", "AdamWeightDecayDynamicLR", "AdamWeightDecay"]:
+                raise RuntimeError("Optimizer segmentation does not support optimizer {}".format(self.cls_name))
+            if _get_parallel_mode() not in [ParallelMode.HYBRID_PARALLEL, ParallelMode.DATA_PARALLEL,
+                                            ParallelMode.AUTO_PARALLEL]:
+                raise RuntimeError("Optimizer segmentation does not support parallel mode {}".format
+                                   (_get_parallel_mode()))
+            self.dev_num = _get_device_num()
+            if self.dev_num > self.param_length:
+                raise RuntimeError("Optimizer segmentation can not be applied when the number of parameters {} is"
+                                   " less than the number of devices {}".format(self.param_length, self.dev_num))
+            self.param_rank = self._get_parameter_group_id()
+            self.optim_filter = tuple(map(lambda x: x == _get_global_rank(), self.param_rank))
+            self.param_names = []
+            for param in self.parameters:
+                self.param_names.append(param.name)
+        else:
+            self.optim_filter = (True,) * self.param_length
+
     def decay_weight(self, gradients):
         """
         Weight decay.
@@ -384,6 +408,51 @@ class Optimizer(Cell):
                 lr = self.learning_rate
         return lr
 
+    def _get_parameter_group_id(self):
+        """
+        Get the parameter partition group id, which is less than the number of devices.
+
+        Returns:
+            tuple, the group id tuple of parameters.
+        """
+        rank_list = ()
+        count = 0
+        for _ in range(self.param_length):
+            rank_list = rank_list + (count,)
+            count = count + 1
+            if count == self.dev_num:
+                count = 0
+        return rank_list
+
+    def broadcast_params(self, optim_result):
+        """
+        Apply Broadcast operations in the sequential order of parameter groups.
+
+        Returns:
+             bool, the status flag.
+        """
+        param_group = []
+        key_group = []
+        for _ in range(self.dev_num):
+            param_group.append(F.make_tuple())
+            key_group.append(F.make_tuple())
+        for i in range(self.param_length):
+            param_group[self.param_rank[i]] = param_group[self.param_rank[i]] + (optim_result[i],)
+            key = P.MakeRefKey(self.param_names[i])()
+            key_group[self.param_rank[i]] = key_group[self.param_rank[i]] + (key,)
+        new_param_group = []
+        for root in range(self.dev_num):
+            ops = P.Broadcast(root)
+            next_params = ops(param_group[root])
+            new_param_group.append(next_params)
+            for i in range(F.tuple_len(next_params)):
+                F.assign(key_group[root][i], next_params[i])
+        status = True
+        for i in range(self.dev_num - 1):
+            status = F.control_depend(new_param_group[i][0], new_param_group[i+1])
+
+        return status
+
     def construct(self, *hyper_params):
         raise NotImplementedError
 

mindspore/ops/composite/multitype_ops/setitem_impl.py

@@ -85,6 +85,22 @@ def _list_setitem_with_List(data, number_index, value):
     return F.list_setitem(data, number_index, value)
 
 
+@setitem.register("List", "Number", "Tuple")
+def _list_setitem_with_Tuple(data, number_index, value):
+    """
+    Assigns value to list.
+
+    Inputs:
+        data (list): Data of type lis.
+        number_index (Number): Index of data.
+        value (list): Value given.
+
+    Outputs:
+        list, type is same as the element type of data.
+    """
+    return F.list_setitem(data, number_index, value)
+
+
 @setitem.register("Dictionary", "String", "Tensor")
 def _dict_setitem_with_tensor(data, key, value):
     """

mindspore/ops/operations/comm_ops.py

@@ -98,6 +98,7 @@ class AllReduce(PrimitiveWithInfer):
         self.op = op
         self.add_prim_attr('group', _get_group(group))
         self.add_prim_attr('fusion', 0)
+        self.add_prim_attr('index', 0)
 
     def vm_impl(self, x):
         """Implement by vm mode."""

mindspore/ops/operations/other_ops.py

@@ -59,8 +59,7 @@ class Assign(PrimitiveWithInfer):
         return variable
 
     def infer_dtype(self, variable, value):
-        args = {"variable": variable, "value": value}
-        validator.check_tensor_type_same(args, (mstype.bool_,) + mstype.number_type, self.name)
+        # Add a type validation later when we don't have to assign a value to RefKey.
         return variable
 
 

mindspore/parallel/_auto_parallel_context.py

@@ -400,6 +400,23 @@ class _AutoParallelContext:
         self.check_context_handle()
         return self._context_handle.get_global_rank_is_set()
 
+    def set_enable_parallel_optimizer(self, enable_parallel_optimizer):
+        """
+        Set enable/disable parallel optimizer.
+
+        Args:
+            set_enable_parallel_optimizer (bool): Enable/disable parallel optimizer.
+        """
+        self.check_context_handle()
+        if not isinstance(enable_parallel_optimizer, bool):
+            raise TypeError('enable_parallel_optimizer is invalid type')
+        self._context_handle.set_enable_parallel_optimizer(enable_parallel_optimizer)
+
+    def get_enable_parallel_optimizer(self):
+        """Get parallel optimizer flag."""
+        self.check_context_handle()
+        return self._context_handle.get_enable_parallel_optimizer()
+
     def reset(self):
         """Reset all settings."""
         self.check_context_handle()
@@ -433,7 +450,8 @@ _set_auto_parallel_context_func_map = {
     "parameter_broadcast": auto_parallel_context().set_parameter_broadcast,
     "strategy_ckpt_load_file": auto_parallel_context().set_strategy_ckpt_load_file,
     "strategy_ckpt_save_file": auto_parallel_context().set_strategy_ckpt_save_file,
-    "full_batch": auto_parallel_context().set_full_batch}
+    "full_batch": auto_parallel_context().set_full_batch,
+    "enable_parallel_optimizer": auto_parallel_context().set_enable_parallel_optimizer}
 
 
 _get_auto_parallel_context_func_map = {
@@ -447,13 +465,15 @@ _get_auto_parallel_context_func_map = {
     "parameter_broadcast": auto_parallel_context().get_parameter_broadcast,
     "strategy_ckpt_load_file": auto_parallel_context().get_strategy_ckpt_load_file,
     "strategy_ckpt_save_file": auto_parallel_context().get_strategy_ckpt_save_file,
-    "full_batch": auto_parallel_context().get_full_batch}
+    "full_batch": auto_parallel_context().get_full_batch,
+    "enable_parallel_optimizer": auto_parallel_context().get_enable_parallel_optimizer}
 
 
 @args_type_check(device_num=int, global_rank=int, mirror_mean=bool, cast_before_mirror=bool,
                  loss_repeated_mean=bool, parallel_mode=str, auto_parallel_search_mode=str,
                  parameter_broadcast=bool, strategy_ckpt_load_file=str,
-                 strategy_ckpt_save_file=str, full_batch=bool)
+                 strategy_ckpt_save_file=str, full_batch=bool, enable_parallel_optimizer=bool)
+
 def _set_auto_parallel_context(**kwargs):
     """
     Set auto parallel context.
@@ -493,6 +513,7 @@ def _set_auto_parallel_context(**kwargs):
         strategy_ckpt_load_file (str): The path to load parallel strategy checkpoint. Default: ''
         strategy_ckpt_save_file (str): The path to save parallel strategy checkpoint. Default: ''
         full_batch (bool): Whether to load the whole batch on each device. Default: False.
+        enable_parallel_optimizer (bool): Enable using optimizer segmentation or noe. Default: False.
 
     Raises:
         ValueError: If input key is not attribute in auto parallel context.
@@ -535,5 +556,6 @@ def _reset_auto_parallel_context():
     - parameter_broadcast: False.
     - strategy_ckpt_load_file: ""
     - strategy_ckpt_save_file: ""
+    - enable_parallel_optimizer: False
     """
     auto_parallel_context().reset()

tests/ut/cpp/parallel/step_parallel_test.cc

@@ -327,6 +327,9 @@ TEST_F(TestStepParallel, CreatOpInstance) {
         } else if (name == "instance_name") {
           parse::ConvertData(py::cast<py::object>(item.second), &converted_ret);
           ASSERT_EQ(converted_ret->ToString(), "test");
+        } else if (name == "index") {
+          parse::ConvertData(py::cast<py::object>(item.second), &converted_ret);
+          ASSERT_EQ(converted_ret->ToString(), "0");
         } else {
           MS_LOG(EXCEPTION) << "Test failed";
         }

tests/ut/python/ops/test_signature.py

@@ -16,7 +16,6 @@
 test assign sub
 """
 import numpy as np
-import pytest
 
 import mindspore.context as context
 import mindspore.nn as nn
@@ -36,27 +35,6 @@ class AssignW(nn.Cell):
         return x
 
 
-class Net(nn.Cell):
-    def __init__(self):
-        super(Net, self).__init__()
-        self.b = Parameter(initializer('ones', [5]), name='b')
-        self.assign = AssignW()
-
-    def construct(self, value):
-        return self.assign(self.b, value)
-
-
-def test_assign_through_cell():
-    context.set_context(mode=context.GRAPH_MODE)
-    net = Net()
-    net.to_float(ms.float16)
-    net.add_flags_recursive(fp16=False)
-    input_data = Tensor(np.ones([5]).astype(np.float32))
-    net(input_data)
-    with pytest.raises(TypeError):
-        net(None)
-
-
 class AssignOp(nn.Cell):
     def __init__(self):
         super(AssignOp, self).__init__()

tests/ut/python/parallel/test_parallel_optimizer.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 pytest
+
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.common.api import _executor
+from mindspore.nn import TrainOneStepCell, WithLossCell
+from mindspore.nn.optim import Adam, AdamWeightDecay, AdamWeightDecayDynamicLR, Lamb
+from mindspore.ops import operations as P
+from mindspore.parallel._auto_parallel_context import auto_parallel_context
+from mindspore import context
+
+
+class Net(nn.Cell):
+    """Net definition"""
+
+    def __init__(self):
+        super(Net, self).__init__()
+        self.fc1 = nn.Dense(128, 768, activation='relu')
+        self.fc2 = nn.Dense(128, 768, activation='relu')
+        self.fc3 = nn.Dense(128, 768, activation='relu')
+        self.fc4 = nn.Dense(768, 768, activation='relu')
+        self.relu4 = nn.ReLU()
+        self.relu5 = nn.ReLU()
+        self.transpose = P.Transpose()
+        self.matmul1 = P.MatMul()
+        self.matmul2 = P.MatMul()
+
+    def construct(self, x):
+        q = self.fc1(x)
+        k = self.fc2(x)
+        v = self.fc3(x)
+        k = self.transpose(k, (1, 0))
+        c = self.relu4(self.matmul1(q, k))
+        s = self.relu5(self.matmul2(c, v))
+        s = self.fc4(s)
+        return s
+
+
+def test_AdamWeightDecayDynamicLR():
+    """ test_AdamWeightDecayDynamicLR """
+    auto_parallel_context().set_enable_parallel_optimizer(True)
+    context.set_auto_parallel_context(parallel_mode="data_parallel", device_num=2)
+    inputs = Tensor(np.ones([32, 128]).astype(np.float32))
+    label = Tensor(np.zeros([32, 768]).astype(np.float32))
+    net = Net()
+    net.set_train()
+    loss = nn.SoftmaxCrossEntropyWithLogits()
+    optimizer = AdamWeightDecayDynamicLR(net.trainable_params(), decay_steps=20, learning_rate=0.1)
+
+    net_with_loss = WithLossCell(net, loss)
+    train_network = TrainOneStepCell(net_with_loss, optimizer)
+    _executor.compile(train_network, inputs, label)
+
+
+def test_AdamWeightDecay():
+    """ test_AdamWeightDecayDynamicLR """
+    auto_parallel_context().set_enable_parallel_optimizer(True)
+    context.set_auto_parallel_context(parallel_mode="data_parallel", device_num=2)
+    inputs = Tensor(np.ones([32, 128]).astype(np.float32))
+    label = Tensor(np.zeros([32, 768]).astype(np.float32))
+    net = Net()
+    net.set_train()
+    loss = nn.SoftmaxCrossEntropyWithLogits()
+    optimizer = AdamWeightDecay(net.trainable_params(), learning_rate=0.1)
+
+    net_with_loss = WithLossCell(net, loss)
+    train_network = TrainOneStepCell(net_with_loss, optimizer)
+    _executor.compile(train_network, inputs, label)
+
+
+def test_lamb_compile():
+    """ test_Lamb_compile """
+    auto_parallel_context().set_enable_parallel_optimizer(True)
+    context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=2)
+    inputs = Tensor(np.ones([32, 128]).astype(np.float32))
+    label = Tensor(np.zeros([32, 768]).astype(np.float32))
+    net = Net()
+    net.set_train()
+    loss = nn.SoftmaxCrossEntropyWithLogits()
+    optimizer = Lamb(net.trainable_params(), decay_steps=10)
+
+    net_with_loss = WithLossCell(net, loss)
+    train_network = TrainOneStepCell(net_with_loss, optimizer)
+    _executor.compile(train_network, inputs, label)
+
+
+def test_edge_case():
+    """ test_edge_case """
+    auto_parallel_context().set_enable_parallel_optimizer(True)
+    net = Net()
+    with pytest.raises(RuntimeError):
+        context.set_auto_parallel_context(parallel_mode="stand_alone")
+        Lamb(net.trainable_params(), decay_steps=10)
+    with pytest.raises(RuntimeError):
+        Adam(net.trainable_params(), learning_rate=0.1)
+    with pytest.raises(RuntimeError):
+        context.set_auto_parallel_context(device_num=16)
+        Lamb(net.trainable_params(), decay_steps=10)

tests/ut/python/parallel/test_set_auto_parallel_context.py

@@ -81,6 +81,10 @@ def test_set_auto_parallel_context():
     with pytest.raises(ValueError):
         set_algo_parameters(tensor_slice_align_size=1025)
 
+    auto_parallel_context().set_enable_parallel_optimizer(True)
+    assert auto_parallel_context().get_enable_parallel_optimizer() is True
+    assert not auto_parallel_context().get_all_reduce_fusion_split_indices()
+
 
 def test_reset_auto_parallel_context():
     context.reset_auto_parallel_context()