!49667 Add some side effect test cases with Fallback runtime.

Merge pull request !49667 from Margaret_wangrui/auto_monad_fallback

mindspore/ccsrc/pipeline/jit/static_analysis/prim.cc

@@ -1369,7 +1369,7 @@ EvalResultPtr InterpretGetAttrNode(const AbstractBasePtrList &args_abs_list, con
   (void)value_list.emplace_back(owner_node);
   const auto value_tuple_node = fg->NewCNode(value_list);
 
-  const auto getattr_node = fg->NewCNodeInOrder(
+  const auto getattr_node = fg->NewCNode(
     {NewValueNode(prim::kPrimPyExecute), NewValueNode(script_getattr_str), NewValueNode(key_tuple), value_tuple_node});
   getattr_node->set_debug_info(cnode->debug_info());
   MS_LOG(DEBUG) << "getattr_node: " << getattr_node->DebugString(debug_recursive_level);
@@ -1378,7 +1378,7 @@ EvalResultPtr InterpretGetAttrNode(const AbstractBasePtrList &args_abs_list, con
   auto eng = out_conf->engine();
   MS_EXCEPTION_IF_NULL(eng);
   auto fn_conf = eng->MakeConfig(getattr_node, out_conf->context(), out_conf->func_graph());
-  return eng->ForwardConfig(out_conf, fn_conf);
+  return eng->ForwardConfig(out_conf, fn_conf, false);
 }
 
 EvalResultPtr StaticGetterInferred(const ValuePtr &value, const ConfigPtr &data_conf, const AnfNodeConfigPtr &old_conf,
@@ -2806,7 +2806,7 @@ class RaiseEvaluator : public TransitionPrimEvaluator {
     const auto key_value_tuple = cur_graph->NewCNodeInOrder(values_);
 
     // Build the PyExecute node for raise error.
-    const auto raise_error_node = cur_graph->NewCNodeInOrder(
+    const auto raise_error_node = cur_graph->NewCNode(
       {NewValueNode(prim::kPrimPyExecute), NewValueNode(script_str), key_value_name_tuple, key_value_tuple});
     auto none_type = std::make_shared<TypeNone>();
     raise_error_node->set_user_data<Type>("__py_execute_no_return_type__", none_type);
@@ -2814,7 +2814,7 @@ class RaiseEvaluator : public TransitionPrimEvaluator {
     AnalysisEnginePtr eng = out_conf->engine();
     MS_EXCEPTION_IF_NULL(eng);
     AnfNodeConfigPtr fn_conf = eng->MakeConfig(raise_error_node, out_conf->context(), out_conf->func_graph());
-    return eng->ForwardConfig(out_conf, fn_conf);
+    return eng->ForwardConfig(out_conf, fn_conf, false);
   }
 
  private:

tests/st/fallback/test_graph_fallback_side_effect.py

@@ -0,0 +1,199 @@
+# Copyright 2023 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.
+# ============================================================================
+""" test graph JIT Fallback runtime feature """
+
+import pytest
+import numpy as np
+import mindspore as ms
+from mindspore import Tensor
+from mindspore.common.parameter import Parameter
+
+
+ms.set_context(mode=ms.GRAPH_MODE)
+
+
+class UserDefinedNet:
+    def __init__(self):
+        self.value = 10
+
+    def __call__(self, x):
+        return self.value * x
+
+
+class UNet(ms.nn.Cell):
+    def __init__(self, net):
+        super().__init__()
+        self.net = net
+        self.para = Parameter(Tensor(2, dtype=ms.float64), name='para')
+
+    def construct(self, x):
+        out = x * self.para
+        print("out:", out)
+        out = self.net(x) + self.para
+        self.para = 2 * x
+        return out, self.para + 10
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_fallback_side_effect_assign():
+    """
+    Feature: Fallback runtime side effect.
+    Description: Test execution order in Fallback runtime.
+    Expectation: No error.
+    """
+    net = UNet(UserDefinedNet())
+    x = np.array(10, np.float64)
+    output = net(ms.Tensor(x))
+    print("output:", output)
+    assert output[0].asnumpy() == 102
+    assert output[1].asnumpy() == 30
+
+
+@pytest.mark.skip(reason="No support yet.")
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_fallback_side_effect_dict():
+    """
+    Feature: Fallback runtime side effect.
+    Description: Test execution order in Fallback runtime.
+    Expectation: No error.
+    """
+    class Net(ms.nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.para = Parameter(Tensor(2, dtype=ms.float64), name='para')
+
+        def construct(self, x):
+            out = x * self.para
+            print("out:", out)
+            x = {'a': Tensor(1, dtype=ms.float64), 'b': Tensor(2, dtype=ms.float64)}
+            y = x.get('a') + out
+            z = dict(a=y+self.para)
+            self.para = 2 * y
+            return z, self.para + 2
+
+    net = Net()
+    x = np.array(10, np.float64)
+    out = net(ms.Tensor(x))
+    print("out:", out)
+    assert out[0] == {'a': 23}
+    assert out[1] == 44
+
+
+@pytest.mark.skip(reason="No support yet.")
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_fallback_side_effect_dict_2():
+    """
+    Feature: Fallback runtime side effect.
+    Description: Test execution order in Fallback runtime.
+    Expectation: No error.
+    """
+    class Net(ms.nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.para = Parameter(Tensor(2, dtype=ms.float64), name='para')
+
+        def construct(self, x):
+            out = x * self.para
+            x = {'a': Tensor(1, dtype=ms.float64), 'b': Tensor(2, dtype=ms.float64)}
+            self.para = x.get('a') + out
+            out = x.get('b') - self.para
+            y = {'c': 3, 'b': 4, 'd': self.para + 1}
+            x.update(y)
+            return self.para + out, x
+
+    net = Net()
+    x = np.array(10, np.float64)
+    out = net(ms.Tensor(x))
+    print("out:", out)
+    assert out[0] == 2
+    assert out[1] == {'a': 1, 'b': 4, 'c': 3, 'd': 22}
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_fallback_side_effect_nested_net():
+    """
+    Feature: Fallback runtime side effect.
+    Description: Test execution order in Fallback runtime.
+    Expectation: No error.
+    """
+    class Inner:
+        def __init__(self):
+            self.number = ms.Tensor(2, dtype=ms.float64)
+
+        def act(self, x, y):
+            return self.number * (x + y)
+
+    @ms.jit_class
+    class InnerNet:
+        def __init__(self):
+            self.inner = Inner()
+            self.para = Parameter(Tensor(2, dtype=ms.float64), name='para')
+
+        def renew_para(self, x, y):
+            self.para = x + y
+            return self.para
+
+    class NestedNet(ms.nn.Cell):
+        @ms.jit
+        def construct(self, x, y):
+            out = InnerNet().inner.act(InnerNet().renew_para(x, y) + x, y)
+            out = out + InnerNet().renew_para(out, y)
+            return out
+
+    x = ms.Tensor(2, dtype=ms.float64)
+    y = ms.Tensor(4, dtype=ms.float64)
+    net = NestedNet()
+    output = net(x, y)
+    print("output:", output)
+    assert output == 52
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_fallback_control_flow():
+    """
+    Feature: Fallback runtime side effect.
+    Description: Test execution order in Fallback runtime.
+    Expectation: No error.
+    """
+    class Net(ms.nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.para = Parameter(Tensor(2, dtype=ms.float64), name='para')
+
+        def construct(self, x):
+            out = x * self.para
+            x = {'a': Tensor(1, dtype=ms.float64), 'b': Tensor(2, dtype=ms.float64)}
+            self.para = x.get('a')
+            if self.para > 0:
+                out = x.get('b') - self.para
+            return self.para, out, x
+
+    net = Net()
+    x = np.array(10, np.float64)
+    out = net(ms.Tensor(x))
+    print("out:", out)
+    assert out[0] == 1
+    assert out[1] == 1
+    assert out[2] == {'a': 1, 'b': 2}