Ascend control sink testcase

Signed-off-by: Nzhoufeng <zhoufeng54@huawei.com>

tests/st/control/test_ascend_control_sink.py

+# 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.
+# ============================================================================
+""" test_ascend_control_sink """
+import pytest
+import numpy as np
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore.ops import operations as op
+from mindspore.common import dtype as mstype
+from mindspore.common.tensor import Tensor
+from mindspore.common.parameter import Parameter
+from mindspore.common.initializer import initializer
+
+
+class ControlSimpleIf(nn.Cell):
+    def __init__(self):
+        super().__init__()
+        self.addn = op.AddN()
+
+    def construct(self, x, y, z, input1, input2):
+        addn1 = self.addn([input1, input1, input1])
+        addn2 = self.addn([input2, input2, input2])
+        addn11 = self.addn([addn1, addn1, addn1])
+        addn22 = self.addn([addn2, addn2, addn2])
+        cond1 = x > y
+        cond2 = y > z
+        # dodge pylint
+        if cond1 and cond2:
+            out = self.addn([addn11, addn11])
+        else:
+            out = self.addn([addn22, addn22])
+        out_me = self.addn([out, input1])
+        return out_me
+
+
+class ControlSimpleIfWithAssign(nn.Cell):
+    def __init__(self, input_shape):
+        super().__init__()
+        self.addn = op.AddN()
+        self.assign = op.Assign()
+        self.input_data = Parameter(initializer(1, input_shape, mstype.float32), name="var")
+
+    def construct(self, x, y, input_data):
+        if x > y:
+            out = self.addn([input_data, input_data, input_data])
+        else:
+            out = self.assign(self.input_data, input_data)
+        return out
+
+
+class ControlIfinIf(nn.Cell):
+    def __init__(self):
+        super().__init__()
+
+    def construct(self, x, y):
+        if x > y:
+            x = x + 1
+            if y < 0:
+                y = y + 1
+            else:
+                y = y + 2
+        else:
+            x = x + 2
+        x = x + y
+        return x
+
+
+class ControlIfbyIfbyIf(nn.Cell):
+    def __init__(self):
+        super().__init__()
+        self.addn = op.AddN()
+
+    def construct(self, x, y, cond1, cond2, input_data):
+        tri_in = self.addn([input_data, input_data, input_data])
+        if x > y:
+            addn_1 = self.addn([tri_in, tri_in])
+        else:
+            addn_1 = self.addn([tri_in, tri_in, tri_in])
+        if cond1:
+            addn_2 = self.addn([addn_1, addn_1])
+        else:
+            addn_2 = self.addn([addn_1, addn_1, addn_1])
+        if cond2:
+            out = self.addn([addn_2, addn_2, addn_2])
+        else:
+            out = self.addn([addn_2, addn_2])
+        return out
+
+
+class ControlMixedWhileIf(nn.Cell):
+    def __init__(self):
+        super().__init__()
+
+    def construct(self, x, y):
+        y = y + 4
+        while x < y:
+            if 2 * x < y:
+                x = x + 1
+            else:
+                x = x + 2
+        x = x + 3
+        return x
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_simple_if():
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    x = np.array(3).astype(np.float32)
+    y = np.array(2).astype(np.float32)
+    z = np.array(3).astype(np.float32)
+    input_shape = (127, 7, 53, 31)
+    input1 = np.random.randn(*input_shape).astype(np.float32)
+    input2 = np.random.randn(*input_shape).astype(np.float32)
+    net = ControlSimpleIf()
+    output = net(Tensor(x), Tensor(y), Tensor(z), Tensor(input1), Tensor(input2))
+    expect = input2 * 3 * 3 * 2 + input1
+    assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_simple_if_with_assign():
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    x = np.array(0).astype(np.float32)
+    y = np.array(1).astype(np.float32)
+    input_shape = (127, 7, 53, 31)
+    input_data = np.random.randn(*input_shape).astype(np.float32)
+    net = ControlSimpleIfWithAssign(input_shape)
+    output = net(Tensor(x), Tensor(y), Tensor(input_data))
+    expect = input_data
+    assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_if_in_if():
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    x = np.array(2.345678).astype(np.float32)
+    y = np.array(1.234567).astype(np.float32)
+    net = ControlIfinIf()
+    output = net(Tensor(x), Tensor(y))
+    expect = x + y + 3
+    assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_if_by_if_by_if():
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    x = np.array(2.345678).astype(np.float32)
+    y = np.array(1.234567).astype(np.float32)
+    cond1 = np.array(True).astype(np.bool)
+    cond2 = np.array(False).astype(np.bool)
+    input_shape = (127, 7, 53, 31)
+    input_data = np.random.randn(*input_shape).astype(np.float32)
+    net = ControlIfbyIfbyIf()
+    output = net(Tensor(x), Tensor(y), Tensor(cond1), Tensor(cond2), Tensor(input_data))
+    expect = input_data * 3 * 2 * 2 * 2
+    assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_mixed_while_if():
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    x = np.array(2).astype(np.int32)
+    y = np.array(14).astype(np.int32)
+    net = ControlMixedWhileIf()
+    output = net(Tensor(x), Tensor(y))
+    expect = np.array(22).astype(np.int32)
+    assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)