set fullname for reshape after reshape-elimination

mindspore/ccsrc/frontend/optimizer/irpass/reshape_eliminate.h

@@ -95,6 +95,7 @@ class TwoReshapeEliminater : public AnfVisitor {
     if (fg != nullptr && x_ != nullptr && shape_ != nullptr) {
       auto new_node = fg->NewCNode({NewValueNode(prim_), x_, shape_});
       new_node->set_abstract(node->abstract());
+      new_node->set_fullname_with_scope(node->fullname_with_scope());
       return new_node;
     }
     return nullptr;

tests/ut/python/parallel/test_auto_parallel_for_loop_reshape.py

@@ -0,0 +1,136 @@
+# 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
+import mindspore.nn as nn
+from mindspore.ops import operations as P, functional as F
+from mindspore.common.initializer import initializer
+import mindspore.common.dtype as mstype
+from mindspore.common.api import _executor
+from tests.dataset_mock import MindData
+
+
+class Dataset(MindData):
+    def __init__(self, predict, label, length=3):
+        super(Dataset, self).__init__(size=length)
+        self.predict = predict
+        self.label = label
+        self.index = 0
+        self.length = length
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        if self.index >= self.length:
+            raise StopIteration
+        self.index += 1
+        return self.predict, self.label
+
+    def reset(self):
+        self.index = 0
+
+
+class LayerNorm(nn.Cell):
+    def __init__(self, normalized_shape, eps=1e-5):
+        super(LayerNorm, self).__init__()
+        self.gamma = Parameter(initializer('ones', normalized_shape), name="gamma")
+        self.beta = Parameter(initializer('zeros', normalized_shape), name="beta")
+        self.mean = P.ReduceMean(keep_dims=True)
+        self.eps = eps
+        self.sub = P.Sub()
+        self.add = P.TensorAdd()
+        self.mul = P.Mul()
+        self.div = P.RealDiv()
+
+        self.reshape = P.Reshape()
+        self.shape = P.Shape()
+
+    def construct(self, x):
+        x_origin_shape = self.shape(x)
+        x_target_shape = x_origin_shape[:-1]
+        x_shape = x_origin_shape + (1,)
+        x = self.reshape(x, x_shape)
+        x = self.reshape(x, x_target_shape)
+        mean = self.mean(x, -1)
+        variance = self.mean(F.square(self.sub(x, mean)))
+        output = self.div(self.sub(x, mean), F.sqrt(self.add(variance, self.eps)))
+        rescaled_output = self.add(self.mul(output, self.gamma), self.beta)
+        output_shape = self.shape(rescaled_output) + (1,)
+        rescaled_output = self.reshape(rescaled_output, output_shape)
+        return rescaled_output
+
+
+class SubNet(Cell):
+    def __init__(self, index):
+        super().__init__()
+        self.relu = P.ReLU()
+        self.layernorm1 = LayerNorm((128,)).to_float(mstype.float32)
+
+    def construct(self, x):
+        x = self.layernorm1(x)
+        out = self.relu(x)
+        return out
+
+
+class Net(Cell):
+    def __init__(self, mul_weight, num_layers, strategy1=None, strategy2=None):
+        super().__init__()
+        self.mul = P.Mul().shard(strategy1)
+        self.neg = P.Neg().shard(strategy2)
+        self.mul_weight = Parameter(mul_weight, "w1")
+        self.num_layers = num_layers
+        self.layers = nn.CellList()
+        for i in range(num_layers):
+            self.layers.append(SubNet(i))
+
+    def construct(self, x):
+        for i in range(self.num_layers):
+            x = self.layers[i](x)
+        out = self.mul(x, self.mul_weight)
+        out = self.neg(out)
+        return out
+
+
+class Full(Cell):
+    def __init__(self, mul_weight, num_layers, strategy1=None, strategy2=None):
+        super().__init__()
+        self.network = Net(mul_weight, num_layers, strategy1, strategy2)
+        self.relu = P.ReLU()
+
+    def construct(self, x):
+        out = self.network(x)
+        out = self.relu(out)
+        return out
+
+
+_x = Tensor(np.ones([512, 128, 1]), dtype=ms.float32)
+_b = Tensor(np.ones([32]), dtype=ms.int32)
+_w1 = Tensor(np.ones([512, 128, 1]), dtype=ms.float32)
+
+
+def test_auto_parallel():
+    context.set_context(save_graphs=True)
+    context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=16, global_rank=0)
+    net = Full(_w1, 3)
+    net.set_auto_parallel()
+    net.set_train()
+    _executor.compile(net, _x, phase='train')
+    num_ops = _executor._get_num_parallel_ops(net)
+    expected_num = 16
+    assert num_ops == expected_num