add parallel op for square

mindspore/ccsrc/parallel/dynamic_creator.h

@@ -128,6 +128,7 @@ REGISTER(BatchMatMulInfo);
 REGISTER(ExpandDimsInfo);
 REGISTER(SqueezeInfo);
 REGISTER(SigmoidCrossEntropyWithLogitsInfo);
+REGISTER(SquareInfo);
 }  // namespace parallel
 }  // namespace mindspore
 

mindspore/ccsrc/parallel/ops_info/activation_info.h

@@ -203,6 +203,14 @@ class SqueezeInfo : public ActivationOther {
  private:
   ValueTuplePtr axis_;
 };
+
+class SquareInfo : public ActivationOther {
+ public:
+  SquareInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
+             const PrimitiveAttrs& attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~SquareInfo() override = default;
+};
 }  // namespace parallel
 }  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_PARALLEL_OPS_INFO_ACTIVATION_INFO_H_

mindspore/ccsrc/parallel/ops_info/ops_utils.h

@@ -202,9 +202,10 @@ constexpr char SQRT[] = "Sqrt";
 constexpr char ASSIGN[] = "Assign";
 constexpr char GET_NEXT[] = "GetNext";
 constexpr char SQUEEZE[] = "Squeeze";
-constexpr char Neg[] = "Neg";
+constexpr char NEG[] = "Neg";
 constexpr char BATCH_MATMUL[] = "BatchMatMul";
 constexpr char EXPAND_DIMS[] = "ExpandDims";
+constexpr char SQUARE[] = "Square";
 
 // Parallel don't care
 constexpr char TUPLE_GETITEM[] = "tuple_getitem";

mindspore/ccsrc/parallel/step_auto_parallel.cc

@@ -104,13 +104,14 @@ std::vector<std::string> splittable_op_ = {MATMUL,
                                            SQRT,
                                            GET_NEXT,
                                            CAST,
-                                           Neg,
+                                           NEG,
+                                           SQUARE,
                                            BATCH_MATMUL,
                                            EXPAND_DIMS,
                                            SQUEEZE};
 
-std::vector<std::string> elementwise_op_ = {ACTIVATION, GELU, TANH, SOFTMAX, LOG_SOFTMAX, RELU, SQRT,
-                                            CAST,       POW,  EXP,  LOG,     COS,         ACOS, LOGICALNOT};
+std::vector<std::string> elementwise_op_ = {ACTIVATION, GELU, TANH, SOFTMAX, LOG_SOFTMAX, RELU,       SQRT, CAST,
+                                            POW,        EXP,  LOG,  COS,     ACOS,        LOGICALNOT, NEG,  SQUARE};
 
 bool StepAutoParallel(const FuncGraphPtr &root, const opt::OptimizerPtr &) {
   MS_EXCEPTION_IF_NULL(root);

tests/ut/python/parallel/test_square.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.
+
+import numpy as np
+import mindspore as ms
+from mindspore import context, Tensor, Parameter
+from mindspore.nn import Cell, TrainOneStepCell, Momentum
+from mindspore.ops import operations as P
+from mindspore.common.api import _executor
+
+
+class Net(Cell):
+    def __init__(self, mul_weight, strategy1=None, strategy2=None):
+        super(Net, self).__init__()
+        self.mul = P.Mul().set_strategy(strategy1)
+        self.square = P.Square().set_strategy(strategy2)
+        self.mul2 = P.Mul().set_strategy(strategy1)
+        self.mul_weight = Parameter(mul_weight, "w1")
+
+    def construct(self, x, b):
+        out = self.mul(x, self.mul_weight)
+        out = self.square(out)
+        out = self.mul2(out, b)
+        return out
+
+
+_x = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+_w1 = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+_b = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+
+
+def compile_net(net):
+    optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
+    train_net = TrainOneStepCell(net, optimizer)
+    _executor.compile(train_net, _x,  _b)
+    context.reset_auto_parallel_context()
+
+
+def test_square_data_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((16, 1, 1), (16, 1, 1))
+    strategy2 = ((16, 1, 1), )
+    net = Net(_w1, strategy1, strategy2)
+    compile_net(net)
+
+
+def test_square_model_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((1, 1, 16), (1, 1, 16))
+    strategy2 = ((1, 1, 16), )
+    net = Net(_w1, strategy1, strategy2)
+    compile_net(net)
+
+
+def test_square_hybrid_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((2, 2, 4), (2, 2, 4))
+    strategy2 = ((2, 2, 4), )
+    net = Net(_w1, strategy1, strategy2)
+    compile_net(net)
+
+
+def test_square_auto_parallel():
+    context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=16, global_rank=0)
+    net = Net(_w1)
+    compile_net(net)
+
+
+def test_square_repeat_calc():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((2, 2, 4), (2, 2, 4))
+    strategy2 = ((1, 2, 2), )
+    net = Net(_w1, strategy1, strategy2)
+    compile_net(net)