!9508 [Auto parallel] Set CNODE's fullname when cloning

From: @xiaoda_zh
Reviewed-by: @kisnwang,@stsuteng
Signed-off-by: @stsuteng

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

@@ -95,6 +95,9 @@ 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());
+      if (node->scope() != kDefaultScope) {
+        new_node->set_scope(node->scope());
+      }
       new_node->set_fullname_with_scope(node->fullname_with_scope());
       return new_node;
     }

mindspore/ccsrc/frontend/parallel/step_auto_parallel.cc

@@ -689,6 +689,7 @@ Status ConstructCostGraphNodesByUniqueIdTC(const std::vector<AnfNodePtr> &all_no
         cnode->set_user_data<OperatorInfo>(current_op_ptr);
         MS_LOG(INFO) << "The CNode with UniqueId: " << cnode->UniqueId()
                      << " and UniqueIdThroughCopy: " << cnode->UniqueIdThroughCopy()
+                     << ", CNode fullname_with_scope: " << cnode->fullname_with_scope()
                      << " is set OperatorInfo: " << current_op_ptr->name() << ", Primitive: " << prim->name();
       }
     }

mindspore/core/ir/func_graph_cloner.cc

@@ -91,6 +91,9 @@ void Cloner::CloneCNode(const AnfNodePtr &node, const FuncGraphPtr &target) {
   new_node->set_inputs_value(old_node->inputs_value());
   ScopePtr scope = (node->scope() != kDefaultScope) ? node->scope() : this->scope();
   new_node->set_scope(scope);
+  if (IsPrimitiveCNode(old_node, nullptr) && new_node->scope() == kDefaultScope) {
+    new_node->set_fullname_with_scope(old_node->fullname_with_scope());
+  }
   new_node->set_kernel_info(old_node->kernel_info_ptr());
   repl_node_[old_node] = new_node;
   nodes_.emplace_back(old_node, new_node);

tests/ut/python/parallel/test_auto_parallel_arithmetic.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import re
 import numpy as np
 
 import mindspore as ms
@@ -78,9 +79,11 @@ def test_auto_parallel_arithmetic():
     b = Tensor(np.ones([64, 128]), dtype=ms.float32)
     compile_net(net, x, y, b, phase='train')
     strategies = _executor._get_shard_strategy(net)
-    expected_strategies = {'Default/network-Net/FloorDiv-op0': [[2, 4], [2, 4]],
-                           'Default/network-Net/MatMul-op1': [[2, 1], [1, 4]]}
-    assert strategies == expected_strategies
+    for (k, v) in strategies.items():
+        if re.search('FloorDiv-op', k) is not None:
+            assert v == [[2, 4], [2, 4]]
+        elif re.search('MatMul-op', k) is not None:
+            assert v == [[2, 1], [1, 4]]
 
 
 def test_auto_parallel_arithmetic_broadcast_both():
@@ -105,9 +108,11 @@ def test_auto_parallel_arithmetic_broadcast_both():
     b = Tensor(np.ones([1, 64]), dtype=ms.float32)
     compile_net(net, x, y, b, phase='train')
     strategies = _executor._get_shard_strategy(net)
-    expected_strategies = {'Default/network-Net/FloorDiv-op0': [[8, 1], [1, 1]],
-                           'Default/network-Net/MatMul-op1': [[8, 1], [1, 1]]}
-    assert strategies == expected_strategies
+    for (k, v) in strategies.items():
+        if re.search('FloorDiv-op', k) is not None:
+            assert v == [[8, 1], [1, 1]]
+        elif re.search('MatMul-op', k) is not None:
+            assert v == [[8, 1], [1, 1]]
 
 
 def test_auto_parallel_arithmetic_broadcast_right():
@@ -132,9 +137,11 @@ def test_auto_parallel_arithmetic_broadcast_right():
     b = Tensor(np.ones([32]), dtype=ms.float32)
     compile_net(net, x, y, b, phase='train')
     strategies = _executor._get_shard_strategy(net)
-    expected_strategies = {'Default/network-Net/FloorDiv-op0': [[4, 2], [2]],
-                           'Default/network-Net/MatMul-op1': [[4, 1], [1, 2]]}
-    assert strategies == expected_strategies
+    for (k, v) in strategies.items():
+        if re.search('FloorDiv-op', k) is not None:
+            assert v == [[4, 2], [2]]
+        elif re.search('MatMul-op', k) is not None:
+            assert v == [[4, 1], [1, 2]]
 
 
 def test_auto_parallel_arithmetic_broadcast_left():
@@ -159,6 +166,8 @@ def test_auto_parallel_arithmetic_broadcast_left():
     b = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
     compile_net(net, x, y, b, phase="train")
     strategies = _executor._get_shard_strategy(net)
-    expected_strategies = {'Default/network-Net/FloorDiv-op0': [[4, 2], [1, 4, 2]],
-                           'Default/network-Net/MatMul-op1': [[4, 1], [1, 2]]}
-    assert strategies == expected_strategies
+    for (k, v) in strategies.items():
+        if re.search('FloorDiv-op', k) is not None:
+            assert v == [[4, 2], [1, 4, 2]]
+        elif re.search('MatMul-op', k) is not None:
+            assert v == [[4, 1], [1, 2]]

tests/ut/python/parallel/test_auto_parallel_cast.py

@@ -84,9 +84,9 @@ def test_double_star_graph():
     net.set_train()
     _executor.compile(net, x, y, z, w, phase='train')
     strategies = _executor._get_shard_strategy(net)
-    expected_strategies = {'Default/network-Net/Cast-op1': [[8, 1]],
-                           'Default/network-Net/Cast-op3': [[1, 8]],
-                           'Default/network-Net/MatMul-op2': [[8, 1], [1, 1]],
-                           'Default/network-Net/MatMul-op4': [[1, 1], [1, 8]],
-                           'Default/network-Net/MatMul-op0': [[1, 8], [8, 1]]}
+    expected_strategies = {'Default/network-Net/Cast-op5': [[8, 1]],
+                           'Default/network-Net/Cast-op7': [[1, 8]],
+                           'Default/network-Net/MatMul-op6': [[8, 1], [1, 1]],
+                           'Default/network-Net/MatMul-op8': [[1, 1], [1, 8]],
+                           'Default/network-Net/MatMul-op4': [[1, 8], [8, 1]]}
     assert strategies == expected_strategies

tests/ut/python/parallel/test_auto_parallel_transpose.py

@@ -79,8 +79,8 @@ def test_two_matmul_transpose():
     net.set_train()
     _executor.compile(net, x, y, b, phase='train')
     strategies = _executor._get_shard_strategy(net)
-    expected_strategies = {'Default/network-Net/Transpose-op0': [[1, 16]],
-                           'Default/network-Net/Transpose-op1': [[16, 1]],
-                           'Default/network-Net/MatMul-op2': [[16, 1], [1, 1]],
-                           'Default/network-Net/MatMul-op3': [[16, 1], [1, 1]]}
+    expected_strategies = {'Default/network-Net/Transpose-op4': [[1, 16]],
+                           'Default/network-Net/Transpose-op5': [[16, 1]],
+                           'Default/network-Net/MatMul-op7': [[16, 1], [1, 1]],
+                           'Default/network-Net/MatMul-op6': [[16, 1], [1, 1]]}
     assert strategies == expected_strategies

tests/ut/python/parallel/test_auto_parallel_two_matmul.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import re
 import numpy as np
 
 import mindspore as ms
@@ -155,6 +156,6 @@ def test_two_matmul():
     net.set_train()
     _executor.compile(net, x, y, b, phase='train')
     strategies = _executor._get_shard_strategy(net)
-    expected_strategies = {'Default/network-Net/MatMul-op0': [[16, 1], [1, 1]],
-                           'Default/network-Net/MatMul-op1': [[16, 1], [1, 1]]}
-    assert strategies == expected_strategies
+    for (k, v) in strategies.items():
+        if re.search('MatMul-op', k) is not None:
+            assert v == [[16, 1], [1, 1]]