!15695 dont insert VirtualOutput for scalar

From: @yao_yf
Reviewed-by: @yangzhenzhang,@stsuteng
Signed-off-by: @stsuteng

mindspore/ccsrc/frontend/parallel/step_parallel.cc

@@ -1040,14 +1040,17 @@ void InsertVirtualOutput(const FuncGraphPtr &root, const std::vector<AnfNodePtr>
         cnode = node_pair.first->cast<CNodePtr>();
         last_indexs[last_node_index] = size_t(node_pair.second);
       }
+      auto pre_node = cnode->input(last_indexs[last_node_index]);
+      Shapes shape_outputs = GetNodeShape(pre_node);
+      if (shape_outputs[0].empty()) {
+        continue;
+      }
       FuncGraphPtr func_graph = node->func_graph();
       MS_EXCEPTION_IF_NULL(func_graph);
       OperatorParams params;
       OperatorAttrs attrs;
       OperatorArgs args = std::make_pair(attrs, params);
       Operator op = std::make_pair(VIRTUAL_OUTPUT, args);
-      auto pre_node = cnode->input(last_indexs[last_node_index]);
-      Shapes shape_outputs = GetNodeShape(pre_node);
       InsertNode(op, cnode, last_indexs[last_node_index], pre_node, func_graph, VIRTUAL_OUTPUT);
       auto virtual_output_node = cnode->input(last_indexs[last_node_index]);
       AbstractBasePtr virtual_output_abstract = pre_node->abstract()->Clone();

tests/ut/python/parallel/test_virtual_output.py

@@ -97,6 +97,24 @@ class ReshapeMulNet(nn.Cell):
         out = self.mul(weight, self.mul_weight)
         return out
 
+class ParallelMulNet(nn.Cell):
+    def __init__(self, dense_in_channel=2048, dense_out_channel=250):
+        super().__init__()
+        weight_np = np.full((dense_out_channel, dense_in_channel), 0.01, dtype=np.float32)
+        bias_np = np.full((dense_out_channel,), 0.01, dtype=np.float32)
+        self.flat = nn.Flatten()
+        self.dense = nn.Dense(in_channels=dense_in_channel,
+                              out_channels=dense_out_channel,
+                              weight_init=Tensor(weight_np),
+                              bias_init=Tensor(bias_np),
+                              has_bias=True)
+        self.mul = P.Mul()
+    def construct(self, inputs):
+        x = self.flat(inputs)
+        x = self.dense(x)
+        x = self.mul(x, x)
+        return x
+
 def compile_graph(x, net):
     net.set_auto_parallel()
     net.set_train(False)
@@ -104,6 +122,13 @@ def compile_graph(x, net):
     strategies = _executor._get_shard_strategy(net)
     return strategies
 
+def compile_graph_two_input(x, y, net):
+    net.set_auto_parallel()
+    net.set_train(False)
+    _executor.compile(net, x, y, auto_parallel_mode=True)
+    strategies = _executor._get_shard_strategy(net)
+    return strategies
+
 
 def test_dense_relu_semi_auto():
     context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel", full_batch=False)
@@ -250,3 +275,33 @@ def test_reshape_mul_auto():
     for (k, v) in strategies.items():
         if re.search('VirtualOutput-op', k) is not None:
             assert v[0][0] == 1
+
+def test_scalar_output_semi_auto():
+    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel", full_batch=False)
+    net = ParallelMulNet()
+    loss_fn = nn.SoftmaxCrossEntropyWithLogits(reduction='mean')
+    eval_net = nn.WithEvalCell(net, loss_fn)
+    x = Tensor(np.ones([4096, 1, 2, 1024]).astype(np.float32)*0.01)
+    label = Tensor(np.ones([4096, 250]).astype(np.float32)*0.01)
+    strategies = compile_graph_two_input(x, label, eval_net)
+    count = 0
+    for (k, v) in strategies.items():
+        if re.search('VirtualOutput-op', k) is not None:
+            assert v[0][0] == 8
+            count += 1
+    assert count == 1
+
+def test_scalar_output_auto():
+    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="auto_parallel", full_batch=False)
+    net = ParallelMulNet()
+    loss_fn = nn.SoftmaxCrossEntropyWithLogits(reduction='mean')
+    eval_net = nn.WithEvalCell(net, loss_fn)
+    x = Tensor(np.ones([4096, 1, 2, 1024]).astype(np.float32)*0.01)
+    label = Tensor(np.ones([4096, 250]).astype(np.float32)*0.01)
+    strategies = compile_graph_two_input(x, label, eval_net)
+    count = 0
+    for (k, v) in strategies.items():
+        if re.search('VirtualOutput-op', k) is not None:
+            assert v[0][0] == 8
+            count += 1
+    assert count == 1