!22830 args must be compatible with loaded graph

Merge pull request !22830 from lanzhineng/mindir_control_flow

mindspore/ccsrc/pipeline/jit/action.cc

@@ -945,6 +945,19 @@ bool SetMindIRGraphAction(const ResourcePtr &res) {
                          return arg;
                        });
 
+  abstract::AbstractBasePtrList func_args;
+  const auto inputs = fg->get_inputs();
+  (void)std::transform(inputs.begin(), inputs.end(), std::back_inserter(func_args),
+                       [](const AnfNodePtr &arg) -> AbstractBasePtr {
+                         MS_EXCEPTION_IF_NULL(arg);
+                         return arg->abstract()->Broaden();
+                       });
+  if (!AbstractBasePtrListDeepEqual(func_args, broaded_args)) {
+    MS_LOG(EXCEPTION) << "The args is not compatible with the function graph."
+                      << " Please check the args is compatible with the follow: " << abstract::ArgsToString(func_args)
+                      << " The input args:" << abstract::ArgsToString(broaded_args);
+  }
+
   // suppose that there is not KeywordArgument for the top graph
   // get the hyper parameter
   for (const auto &param : fg->parameters()) {

mindspore/ccsrc/transform/express_ir/mindir_exporter.cc

@@ -306,8 +306,7 @@ void IrExportBuilder::SetValueInfoProto(const AnfNodePtr &node, mind_ir::ValueIn
     mind_ir::TensorProto *tensor_proto = value_proto->add_tensor();
     tensor_proto->set_data_type(GetMindirDataType(elem_type->type_id()));
     if (dims.size() == 0) {
-      MS_LOG(DEBUG) << "SetValueInfoProto set default dim 1.";
+      MS_LOG(DEBUG) << "The dim of ValueInfoProto is 0.";
-      tensor_proto->add_dims(1);
     } else {
       for (const auto &dim : dims) {
         MS_LOG(DEBUG) << "SetValueInfoProto dim: " << dim;

tests/st/control/test_recrusive_mindir.py

@@ -0,0 +1,67 @@
+# 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 os
+import numpy as np
+import pytest
+
+import mindspore.nn as nn
+from mindspore import context
+from mindspore.common.tensor import Tensor
+from mindspore.common import dtype as mstype
+from mindspore.train.serialization import export, load
+
+ZERO = Tensor([0], mstype.int32)
+ONE = Tensor([1], mstype.int32)
+
+
+class RecrusiveNet(nn.Cell):
+    def construct(self, x, z):
+        def f(x, z):
+            y = ZERO
+            if x < 0:
+                y = ONE
+            elif x < 3:
+                y = x * f(x - 1, z)
+            elif x < 5:
+                y = x * f(x - 2, z)
+            else:
+                y = f(x - 4, z)
+            z = y + 1 + z
+            return z
+
+        return f(x, z)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.env_onecard
+def test_recrusive():
+    context.set_context(mode=context.GRAPH_MODE)
+    network = RecrusiveNet()
+
+    x = Tensor(np.array([1]).astype(np.float32))
+    y = Tensor(np.array([2]).astype(np.float32))
+    origin_out = network(x, y)
+
+    file_name = "recrusive_net"
+    export(network, x, y, file_name=file_name, file_format='MINDIR')
+    mindir_name = file_name + ".mindir"
+    assert os.path.exists(mindir_name)
+
+    graph = load(mindir_name)
+    loaded_net = nn.GraphCell(graph)
+    outputs_after_load = loaded_net(x, y)
+    assert origin_out == outputs_after_load