Add some side effect test cases with Fallback runtime.
This commit is contained in:
Margaret_wangrui
parent
af4a342cd4
commit
7042618a09
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@ -1369,7 +1369,7 @@ EvalResultPtr InterpretGetAttrNode(const AbstractBasePtrList &args_abs_list, con
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(void)value_list.emplace_back(owner_node);
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const auto value_tuple_node = fg->NewCNode(value_list);
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const auto getattr_node = fg->NewCNodeInOrder(
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const auto getattr_node = fg->NewCNode(
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{NewValueNode(prim::kPrimPyExecute), NewValueNode(script_getattr_str), NewValueNode(key_tuple), value_tuple_node});
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getattr_node->set_debug_info(cnode->debug_info());
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MS_LOG(DEBUG) << "getattr_node: " << getattr_node->DebugString();
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@ -1378,7 +1378,7 @@ EvalResultPtr InterpretGetAttrNode(const AbstractBasePtrList &args_abs_list, con
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auto eng = out_conf->engine();
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MS_EXCEPTION_IF_NULL(eng);
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auto fn_conf = eng->MakeConfig(getattr_node, out_conf->context(), out_conf->func_graph());
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return eng->ForwardConfig(out_conf, fn_conf);
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return eng->ForwardConfig(out_conf, fn_conf, false);
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}
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EvalResultPtr StaticGetterInferred(const ValuePtr &value, const ConfigPtr &data_conf, const AnfNodeConfigPtr &old_conf,
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@ -2807,7 +2807,7 @@ class RaiseEvaluator : public TransitionPrimEvaluator {
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const auto key_value_tuple = cur_graph->NewCNodeInOrder(values_);
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// Build the PyExecute node for raise error.
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const auto raise_error_node = cur_graph->NewCNodeInOrder(
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const auto raise_error_node = cur_graph->NewCNode(
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{NewValueNode(prim::kPrimPyExecute), NewValueNode(script_str), key_value_name_tuple, key_value_tuple});
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auto none_type = std::make_shared<TypeNone>();
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raise_error_node->set_user_data<Type>("__py_execute_no_return_type__", none_type);
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@ -2815,7 +2815,7 @@ class RaiseEvaluator : public TransitionPrimEvaluator {
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AnalysisEnginePtr eng = out_conf->engine();
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MS_EXCEPTION_IF_NULL(eng);
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AnfNodeConfigPtr fn_conf = eng->MakeConfig(raise_error_node, out_conf->context(), out_conf->func_graph());
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return eng->ForwardConfig(out_conf, fn_conf);
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return eng->ForwardConfig(out_conf, fn_conf, false);
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}
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private:
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@ -0,0 +1,199 @@
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# Copyright 2023 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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""" test graph JIT Fallback runtime feature """
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import pytest
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import numpy as np
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import mindspore as ms
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from mindspore import Tensor
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from mindspore.common.parameter import Parameter
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ms.set_context(mode=ms.GRAPH_MODE)
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class UserDefinedNet:
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def __init__(self):
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self.value = 10
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def __call__(self, x):
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return self.value * x
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class UNet(ms.nn.Cell):
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def __init__(self, net):
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super().__init__()
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self.net = net
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self.para = Parameter(Tensor(2, dtype=ms.float64), name='para')
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def construct(self, x):
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out = x * self.para
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print("out:", out)
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out = self.net(x) + self.para
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self.para = 2 * x
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return out, self.para + 10
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fallback_side_effect_assign():
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"""
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Feature: Fallback runtime side effect.
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Description: Test execution order in Fallback runtime.
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Expectation: No error.
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"""
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net = UNet(UserDefinedNet())
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x = np.array(10, np.float64)
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output = net(ms.Tensor(x))
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print("output:", output)
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assert output[0].asnumpy() == 102
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assert output[1].asnumpy() == 30
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@pytest.mark.skip(reason="No support yet.")
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fallback_side_effect_dict():
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"""
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Feature: Fallback runtime side effect.
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Description: Test execution order in Fallback runtime.
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Expectation: No error.
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"""
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class Net(ms.nn.Cell):
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def __init__(self):
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super().__init__()
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self.para = Parameter(Tensor(2, dtype=ms.float64), name='para')
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def construct(self, x):
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out = x * self.para
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print("out:", out)
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x = {'a': Tensor(1, dtype=ms.float64), 'b': Tensor(2, dtype=ms.float64)}
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y = x.get('a') + out
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z = dict(a=y+self.para)
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self.para = 2 * y
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return z, self.para + 2
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net = Net()
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x = np.array(10, np.float64)
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out = net(ms.Tensor(x))
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print("out:", out)
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assert out[0] == {'a': 23}
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assert out[1] == 44
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@pytest.mark.skip(reason="No support yet.")
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fallback_side_effect_dict_2():
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"""
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Feature: Fallback runtime side effect.
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Description: Test execution order in Fallback runtime.
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Expectation: No error.
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"""
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class Net(ms.nn.Cell):
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def __init__(self):
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super().__init__()
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self.para = Parameter(Tensor(2, dtype=ms.float64), name='para')
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def construct(self, x):
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out = x * self.para
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x = {'a': Tensor(1, dtype=ms.float64), 'b': Tensor(2, dtype=ms.float64)}
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self.para = x.get('a') + out
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out = x.get('b') - self.para
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y = {'c': 3, 'b': 4, 'd': self.para + 1}
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x.update(y)
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return self.para + out, x
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net = Net()
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x = np.array(10, np.float64)
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out = net(ms.Tensor(x))
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print("out:", out)
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assert out[0] == 2
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assert out[1] == {'a': 1, 'b': 4, 'c': 3, 'd': 22}
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fallback_side_effect_nested_net():
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"""
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Feature: Fallback runtime side effect.
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Description: Test execution order in Fallback runtime.
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Expectation: No error.
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"""
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class Inner:
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def __init__(self):
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self.number = ms.Tensor(2, dtype=ms.float64)
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def act(self, x, y):
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return self.number * (x + y)
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@ms.jit_class
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class InnerNet:
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def __init__(self):
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self.inner = Inner()
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self.para = Parameter(Tensor(2, dtype=ms.float64), name='para')
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def renew_para(self, x, y):
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self.para = x + y
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return self.para
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class NestedNet(ms.nn.Cell):
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@ms.jit
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def construct(self, x, y):
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out = InnerNet().inner.act(InnerNet().renew_para(x, y) + x, y)
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out = out + InnerNet().renew_para(out, y)
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return out
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x = ms.Tensor(2, dtype=ms.float64)
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y = ms.Tensor(4, dtype=ms.float64)
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net = NestedNet()
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output = net(x, y)
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print("output:", output)
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assert output == 52
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fallback_control_flow():
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"""
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Feature: Fallback runtime side effect.
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Description: Test execution order in Fallback runtime.
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Expectation: No error.
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"""
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class Net(ms.nn.Cell):
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def __init__(self):
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super().__init__()
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self.para = Parameter(Tensor(2, dtype=ms.float64), name='para')
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def construct(self, x):
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out = x * self.para
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x = {'a': Tensor(1, dtype=ms.float64), 'b': Tensor(2, dtype=ms.float64)}
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self.para = x.get('a')
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if self.para > 0:
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out = x.get('b') - self.para
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return self.para, out, x
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net = Net()
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x = np.array(10, np.float64)
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out = net(ms.Tensor(x))
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print("out:", out)
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assert out[0] == 1
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assert out[1] == 1
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assert out[2] == {'a': 1, 'b': 2}
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