!15949 [ME][ControlFlow] Add some control sink testcases

From: @Margaret_wangrui
Reviewed-by: @ginfung,@zh_qh
Signed-off-by: @zh_qh

tests/st/control/inner/test_002_single_for.py

@@ -211,3 +211,48 @@ def test_single_for_04():
 
     assert graph_forward_res == pynative_forward_res
     assert graph_backward_res == pynative_backward_res
+
+
+def test_single_for_05():
+    class SingleForNet(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.mul = P.Mul()
+            self.add = P.Add()
+            self.sub = P.Sub()
+            self.assign = P.Assign()
+            self.param_a = Parameter(Tensor(np.array(5), mstype.int32), name='a')
+            self.param_b = Parameter(Tensor(np.array(2), mstype.int32), name='b')
+
+        def construct(self, x):
+            self.assign(self.param_a, x + self.param_a)
+            for _ in range(0, 3):
+                self.assign(self.param_b, x - self.param_a)
+            return x
+
+    class GradNet(nn.Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.net = net
+
+        def construct(self, *inputs):
+            return grad_all(self.net)(*inputs)
+
+    x = Tensor([6], mstype.int32)
+
+    # graph mode
+    context.set_context(mode=context.GRAPH_MODE)
+    single_for_net = SingleForNet()
+    net = GradNet(single_for_net)
+    graph_forward_res = single_for_net(x)
+    graph_backward_res = net(x)
+
+    # pynative mode
+    context.set_context(mode=context.PYNATIVE_MODE)
+    single_for_net = SingleForNet()
+    net = GradNet(single_for_net)
+    pynative_forward_res = single_for_net(x)
+    pynative_backward_res = net(x)
+
+    assert graph_forward_res == pynative_forward_res
+    assert graph_backward_res == pynative_backward_res

tests/st/control/inner/test_030_for_in_if.py

@@ -219,3 +219,54 @@ def test_for_in_if_04():
 
     assert graph_forward_res == pynative_forward_res
     assert graph_backward_res == pynative_backward_res
+
+
+def test_for_in_if_05():
+    class ForInIfNet(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.param_a = Parameter(Tensor(5, mstype.int32), name='a')
+            self.param_b = Parameter(Tensor(4, mstype.int32), name='b')
+            self.assign = P.Assign()
+
+        def construct(self, x):
+            out = self.param_a
+            x = self.func(x)
+            out *= x
+            return out
+
+        def func(self, x):
+            if self.param_a > self.param_b:
+                self.assign(self.param_a, self.param_b + self.param_a)
+                for _ in range(0, 4):
+                    self.param_a += 1
+                    self.assign(self.param_b, self.param_b - 4)
+            x += self.param_b
+            return x
+
+    class GradNet(nn.Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.net = net
+
+        def construct(self, *inputs):
+            return grad_all(self.net)(*inputs)
+
+    x = Tensor(5, mstype.int32)
+
+    # graph mode
+    context.set_context(mode=context.GRAPH_MODE)
+    for_in_if_net = ForInIfNet()
+    net = GradNet(for_in_if_net)
+    graph_forward_res = for_in_if_net(x)
+    graph_backward_res = net(x)
+
+    # pynative mode
+    context.set_context(mode=context.PYNATIVE_MODE)
+    for_in_if_net = ForInIfNet()
+    net = GradNet(for_in_if_net)
+    pynative_forward_res = for_in_if_net(x)
+    pynative_backward_res = net(x)
+
+    assert graph_forward_res == pynative_forward_res
+    assert graph_backward_res == pynative_backward_res

tests/st/control/inner/test_031_for_in_while.py

@@ -23,7 +23,7 @@ from mindspore.common import dtype as mstype
 grad_all = C.GradOperation(get_all=True)
 context.set_context(device_target="Ascend")
 
-def test_for_in_while():
+def test_for_in_while_01():
     class ForInWhileNet(nn.Cell):
         def __init__(self):
             super().__init__()
@@ -73,3 +73,52 @@ def test_for_in_while():
 
     assert graph_forward_res == pynative_forward_res
     assert graph_backward_res == pynative_backward_res
+
+
+def test_for_in_while_02():
+    class ForInWhileNet(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.mul = P.Mul()
+            self.add = P.Add()
+            self.sub = P.Sub()
+            self.assign = P.Assign()
+            self.param_a = Parameter(Tensor(5, mstype.int32), name='a')
+            self.param_b = Parameter(Tensor(7, mstype.int32), name='b')
+
+        def construct(self, x):
+            self.assign(self.param_a, x + self.param_a)
+            while self.param_a > self.param_b:
+                for _ in range(0, 3):
+                    x = self.add(x, self.param_a + self.param_b)
+                    self.assign(self.param_b, self.param_b + 1)
+            y = self.sub(x, self.param_b)
+            self.assign(self.param_a, y)
+            return x
+
+    class GradNet(nn.Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.net = net
+
+        def construct(self, *inputs):
+            return grad_all(self.net)(*inputs)
+
+    x = Tensor([2], mstype.int32)
+
+    # graph mode
+    context.set_context(mode=context.GRAPH_MODE)
+    for_in_while_net = ForInWhileNet()
+    net = GradNet(for_in_while_net)
+    graph_forward_res = for_in_while_net(x)
+    graph_backward_res = net(x)
+
+    # pynative mode
+    context.set_context(mode=context.PYNATIVE_MODE)
+    for_in_while_net = ForInWhileNet()
+    net = GradNet(for_in_while_net)
+    pynative_forward_res = for_in_while_net(x)
+    pynative_backward_res = net(x)
+
+    assert graph_forward_res == pynative_forward_res
+    assert graph_backward_res == pynative_backward_res

tests/st/control/inner/test_032_for_in_for.py

@@ -23,7 +23,7 @@ from mindspore.common import dtype as mstype
 grad_all = C.GradOperation(get_all=True)
 context.set_context(device_target="Ascend")
 
-def test_for_in_for():
+def test_for_in_for_01():
     class ForInForNet(nn.Cell):
         def __init__(self):
             super().__init__()
@@ -74,3 +74,51 @@ def test_for_in_for():
 
     assert graph_forward_res == pynative_forward_res
     assert graph_backward_res == pynative_backward_res
+
+
+def test_for_in_for_02():
+    class ForInForNet(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.add = P.Add()
+            self.sub = P.Sub()
+            self.assign = P.Assign()
+            self.param_a = Parameter(Tensor(5, mstype.int32), name='a')
+            self.param_b = Parameter(Tensor(11, mstype.int32), name='b')
+
+        def construct(self, x):
+            for _ in range(0, 10):
+                x = x * 2
+                self.assign(self.param_a, x + self.param_a)
+                for _ in range(0, 5):
+                    x = self.add(x, x)
+                    self.param_b += 1
+            y = self.sub(x, self.param_b + self.param_a)
+            return y
+
+    class GradNet(nn.Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.net = net
+
+        def construct(self, *inputs):
+            return grad_all(self.net)(*inputs)
+
+    x = Tensor([2], mstype.int32)
+
+    # graph mode
+    context.set_context(mode=context.GRAPH_MODE)
+    for_in_for_net = ForInForNet()
+    net = GradNet(for_in_for_net)
+    graph_forward_res = for_in_for_net(x)
+    graph_backward_res = net(x)
+
+    # pynative mode
+    context.set_context(mode=context.PYNATIVE_MODE)
+    for_in_for_net = ForInForNet()
+    net = GradNet(for_in_for_net)
+    pynative_forward_res = for_in_for_net(x)
+    pynative_backward_res = net(x)
+
+    assert graph_forward_res == pynative_forward_res
+    assert graph_backward_res == pynative_backward_res

tests/st/control/inner/test_102_if_after_for.py

@@ -24,7 +24,7 @@ grad_all = C.GradOperation(get_all=True)
 context.set_context(device_target="Ascend")
 
 
-def test_if_after_for():
+def test_if_after_for_01():
     class IfAfterForNet(nn.Cell):
         def __init__(self):
             super().__init__()
@@ -76,3 +76,57 @@ def test_if_after_for():
 
     assert graph_forward_res == pynative_forward_res
     assert graph_backward_res == pynative_backward_res
+
+
+def test_if_after_for_02():
+    class IfAfterForNet(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.relu = nn.ReLU()
+            self.mul = P.Mul()
+            self.add = P.Add()
+            self.sub = P.Sub()
+            self.assign = P.Assign()
+            self.param_a = Parameter(Tensor(5, mstype.int32), name='a')
+            self.param_b = Parameter(Tensor(11, mstype.int32), name='b')
+
+        def construct(self, x):
+            self.assign(self.param_a, x + self.param_a)
+            y = self.add(x, self.param_b)
+            for _ in range(0, 2):
+                x = self.sub(x, 2)
+                self.assign(self.param_b, self.param_a + self.param_b - x)
+            self.param_b = self.add(self.param_b, 2)
+            if x < self.param_b:
+                x = y - x
+                y = self.mul(x, self.param_a)
+
+            z = self.relu(x + y)
+            return z
+
+    class GradNet(nn.Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.net = net
+
+        def construct(self, *inputs):
+            return grad_all(self.net)(*inputs)
+
+    x = Tensor([7], mstype.int32)
+
+    # graph mode
+    context.set_context(mode=context.GRAPH_MODE)
+    if_after_for_net = IfAfterForNet()
+    net = GradNet(if_after_for_net)
+    graph_forward_res = if_after_for_net(x)
+    graph_backward_res = net(x)
+
+    # pynative mode
+    context.set_context(mode=context.PYNATIVE_MODE)
+    if_after_for_net = IfAfterForNet()
+    net = GradNet(if_after_for_net)
+    pynative_forward_res = if_after_for_net(x)
+    pynative_backward_res = net(x)
+
+    assert graph_forward_res == pynative_forward_res
+    assert graph_backward_res == pynative_backward_res

tests/st/control/inner/test_302_for_after_for.py

@@ -23,7 +23,7 @@ from mindspore.common import dtype as mstype
 grad_all = C.GradOperation(get_all=True)
 context.set_context(device_target="Ascend")
 
-def test_for_after_for():
+def test_for_after_for_01():
     class ForAfterForNet(nn.Cell):
         def __init__(self):
             super().__init__()
@@ -77,3 +77,56 @@ def test_for_after_for():
 
     assert graph_forward_res == pynative_forward_res
     assert graph_backward_res == pynative_backward_res
+
+
+def test_for_after_for_02():
+    class ForAfterForNet(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.mul = P.Mul()
+            self.add = P.Add()
+            self.sub = P.Sub()
+            self.assign = P.Assign()
+            param_a = np.full((1,), 5, dtype=np.int32)
+            self.param_a = Parameter(Tensor(param_a), name='a')
+            param_b = np.full((1,), 11, dtype=np.int32)
+            self.param_b = Parameter(Tensor(param_b), name='b')
+
+        def construct(self, x):
+            self.assign(self.param_a, x + self.param_a)
+            y = self.add(x, self.param_a)
+            for _ in range(0, 2):
+                x = self.sub(x, 3)
+                self.assign(self.param_b, x + self.param_b)
+            self.param_a = x + y
+            for _ in range(0, 5):
+                y = self.mul(x, self.param_a)
+                x = x + self.param_a
+            return y
+
+    class GradNet(nn.Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.net = net
+
+        def construct(self, *inputs):
+            return grad_all(self.net)(*inputs)
+
+    x = Tensor([7], mstype.int32)
+
+    # graph mode
+    context.set_context(mode=context.GRAPH_MODE)
+    for_after_for_net = ForAfterForNet()
+    net = GradNet(for_after_for_net)
+    graph_forward_res = for_after_for_net(x)
+    graph_backward_res = net(x)
+
+    # pynative mode
+    context.set_context(mode=context.PYNATIVE_MODE)
+    for_after_for_net = ForAfterForNet()
+    net = GradNet(for_after_for_net)
+    pynative_forward_res = for_after_for_net(x)
+    pynative_backward_res = net(x)
+
+    assert graph_forward_res == pynative_forward_res
+    assert graph_backward_res == pynative_backward_res

tests/st/control/inner/test_320_for_after_while_in_if.py

@@ -23,7 +23,7 @@ from mindspore.common import dtype as mstype
 grad_all = C.GradOperation(get_all=True)
 context.set_context(device_target="Ascend")
 
-def test_for_after_while_in_if():
+def test_for_after_while_in_if_01():
     class ForAfterWhileInIfNet(nn.Cell):
         def __init__(self):
             super().__init__()
@@ -90,3 +90,63 @@ def test_for_after_while_in_if():
 
     assert graph_forward_res == pynative_forward_res
     assert graph_backward_res == pynative_backward_res
+
+
+def test_for_after_while_in_if_02():
+    class ForAfterWhileInIfNet(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.mul = P.Mul()
+            self.add = P.Add()
+            self.sub = P.Sub()
+            self.assign = P.Assign()
+            param_a = np.full((1,), 5, dtype=np.int32)
+            self.param_a = Parameter(Tensor(param_a), name='a')
+            param_b = np.full((1,), 2, dtype=np.int32)
+            self.param_b = Parameter(Tensor(param_b), name='b')
+            param_c = np.full((1,), 11, dtype=np.int32)
+            self.param_c = Parameter(Tensor(param_c), name='c')
+
+
+        def construct(self, x, y):
+            self.assign(self.param_a, x + self.param_a)
+            y = self.add(y, self.param_b)
+            if (self.param_b > (y - self.param_a)) and (self.param_b != self.param_a):
+                x = y - self.param_a - self.param_b
+                while self.param_a >= x:
+                    self.assign(self.param_c, self.param_a + 2)
+                    x = x + 2
+                self.param_b = self.sub(y, self.param_b)
+            x = self.mul(self.param_b, self.param_c)
+            for _ in range(0, 4):
+                self.assign(self.param_b, y + self.param_b - x)
+                y = x + self.param_a - self.param_b
+            return y
+
+    class GradNet(nn.Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.net = net
+
+        def construct(self, *inputs):
+            return grad_all(self.net)(*inputs)
+
+    x = Tensor([11], mstype.int32)
+    y = Tensor([7], mstype.int32)
+
+    # graph mode
+    context.set_context(mode=context.GRAPH_MODE)
+    for_after_while_in_if_net = ForAfterWhileInIfNet()
+    net = GradNet(for_after_while_in_if_net)
+    graph_forward_res = for_after_while_in_if_net(x, y)
+    graph_backward_res = net(x, y)
+
+    # pynative mode
+    context.set_context(mode=context.PYNATIVE_MODE)
+    for_after_while_in_if_net = ForAfterWhileInIfNet()
+    net = GradNet(for_after_while_in_if_net)
+    pynative_forward_res = for_after_while_in_if_net(x, y)
+    pynative_backward_res = net(x, y)
+
+    assert graph_forward_res == pynative_forward_res
+    assert graph_backward_res == pynative_backward_res

tests/st/control/inner/test_322_for_after_while_in_for.py

@@ -23,7 +23,7 @@ from mindspore.common import dtype as mstype
 grad_all = C.GradOperation(get_all=True)
 context.set_context(device_target="Ascend")
 
-def test_for_after_while_in_for():
+def test_for_after_while_in_for_01():
     class ForAfterWhileInForNet(nn.Cell):
         def __init__(self):
             super().__init__()
@@ -91,3 +91,63 @@ def test_for_after_while_in_for():
 
     assert graph_forward_res == pynative_forward_res
     assert graph_backward_res == pynative_backward_res
+
+
+def test_for_after_while_in_for_02():
+    class ForAfterWhileInForNet(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.mul = P.Mul()
+            self.add = P.Add()
+            self.sub = P.Sub()
+            self.div = P.Div()
+            self.relu = nn.ReLU()
+            self.assign = P.Assign()
+            param_a = np.full((1,), 5, dtype=np.int32)
+            self.param_a = Parameter(Tensor(param_a), name='a')
+            param_b = np.full((1,), 2, dtype=np.int32)
+            self.param_b = Parameter(Tensor(param_b), name='b')
+            param_c = np.full((1,), 30, dtype=np.int32)
+            self.param_c = Parameter(Tensor(param_c), name='c')
+
+        def construct(self, x, y):
+            self.assign(self.param_a, x + self.param_a)
+            y = self.add(y, self.param_b)
+            for _ in range(0, 10):
+                self.param_b = self.add(self.param_c, self.param_b)
+                while self.param_c > self.param_b:
+                    self.assign(self.param_b, self.param_b + self.param_a + 2)
+                self.param_b = self.sub(y, self.param_b)
+            x = self.mul(self.param_b, self.param_c)
+            for _ in range(0, 4):
+                y = y + self.param_b
+                self.assign(self.param_b, x * 3 - y)
+            return x
+
+    class GradNet(nn.Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.net = net
+
+        def construct(self, *inputs):
+            return grad_all(self.net)(*inputs)
+
+    x = Tensor([11], mstype.int32)
+    y = Tensor([7], mstype.int32)
+
+    # graph mode
+    context.set_context(mode=context.GRAPH_MODE)
+    for_after_while_in_for_net = ForAfterWhileInForNet()
+    net = GradNet(for_after_while_in_for_net)
+    graph_forward_res = for_after_while_in_for_net(x, y)
+    graph_backward_res = net(x, y)
+
+    # pynative mode
+    context.set_context(mode=context.PYNATIVE_MODE)
+    for_after_while_in_for_net = ForAfterWhileInForNet()
+    net = GradNet(for_after_while_in_for_net)
+    pynative_forward_res = for_after_while_in_for_net(x, y)
+    pynative_backward_res = net(x, y)
+
+    assert graph_forward_res == pynative_forward_res
+    assert graph_backward_res == pynative_backward_res

tests/st/control/inner/test_331_for_after_for_in_while.py

@@ -23,7 +23,7 @@ from mindspore.common import dtype as mstype
 grad_all = C.GradOperation(get_all=True)
 context.set_context(device_target="Ascend")
 
-def test_for_after_for_in_while():
+def test_for_after_for_in_while_01():
     class ForAfterForInWhileNet(nn.Cell):
         def __init__(self):
             super().__init__()
@@ -86,3 +86,56 @@ def test_for_after_for_in_while():
 
     assert graph_forward_res == pynative_forward_res
     assert graph_backward_res == pynative_backward_res
+
+
+def test_for_after_for_in_while_02():
+    class ForAfterForInWhileNet(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.mul = P.Mul()
+            self.add = P.Add()
+            self.sub = P.Sub()
+            self.assign = P.Assign()
+            self.param_a = Parameter(Tensor(5, mstype.int32), name='a')
+            self.param_b = Parameter(Tensor(2, mstype.int32), name='b')
+            self.param_c = Parameter(Tensor(-10, mstype.int32), name='c')
+
+        def construct(self, x, y):
+            while self.param_c > x:
+                self.param_b = self.add(self.param_c, self.param_b)
+                for _ in range(0, 20):
+                    self.assign(self.param_b, self.param_a + 2)
+                self.assign(self.param_c, self.param_c - 1)
+                x = x + 2
+            for _ in range(0, 4):
+                self.assign(self.param_c, y + self.param_b)
+            x = self.param_a - x - y
+            return x
+
+    class GradNet(nn.Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.net = net
+
+        def construct(self, *inputs):
+            return grad_all(self.net)(*inputs)
+
+    x = Tensor([11], mstype.int32)
+    y = Tensor([7], mstype.int32)
+
+    # graph mode
+    context.set_context(mode=context.GRAPH_MODE)
+    for_after_for_in_while_net = ForAfterForInWhileNet()
+    net = GradNet(for_after_for_in_while_net)
+    graph_forward_res = for_after_for_in_while_net(x, y)
+    graph_backward_res = net(x, y)
+
+    # pynative mode
+    context.set_context(mode=context.PYNATIVE_MODE)
+    for_after_for_in_while_net = ForAfterForInWhileNet()
+    net = GradNet(for_after_for_in_while_net)
+    pynative_forward_res = for_after_for_in_while_net(x, y)
+    pynative_backward_res = net(x, y)
+
+    assert graph_forward_res == pynative_forward_res
+    assert graph_backward_res == pynative_backward_res

tests/st/control/inner/test_332_for_after_for_in_for.py

@@ -23,7 +23,7 @@ from mindspore.common import dtype as mstype
 grad_all = C.GradOperation(get_all=True)
 context.set_context(device_target="Ascend")
 
-def test_for_after_for_in_for():
+def test_for_after_for_in_for_01():
     class ForAfterForInForNet(nn.Cell):
         def __init__(self):
             super().__init__()
@@ -45,7 +45,7 @@ def test_for_after_for_in_for():
             for _ in range(0, 4):
                 self.param_b = self.add(self.param_c, self.param_b)
                 for _ in range(0, 8):
-                    self.param_b = self.param_a + j
+                    self.param_b = self.param_a + x
                 self.param_c = self.param_a * self.param_b
 
             for _ in range(0, 3):
@@ -82,3 +82,59 @@ def test_for_after_for_in_for():
 
     assert graph_forward_res == pynative_forward_res
     assert graph_backward_res == pynative_backward_res
+
+
+def test_for_after_for_in_for_02():
+    class ForAfterForInForNet(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.mul = P.Mul()
+            self.add = P.Add()
+            self.sub = P.Sub()
+            self.div = P.Div()
+            self.assign = P.Assign()
+            self.param_a = Parameter(Tensor(5, mstype.int32), name='a')
+            self.param_b = Parameter(Tensor(2, mstype.int32), name='b')
+            self.param_c = Parameter(Tensor(20, mstype.int32), name='c')
+
+        def construct(self, x, y):
+            for _ in range(0, 6):
+                self.param_b = self.add(self.param_c, self.param_b)
+                for _ in range(0, 2):
+                    self.assign(self.param_b, self.param_a + x)
+                self.assign(self.param_c, self.param_a * self.param_b)
+
+            for _ in range(0, 3):
+                y = y + self.param_b
+                x = self.relu(self.param_c * 3)
+                self.assign(self.param_b, x - y)
+            z = y + self.param_b
+            return z
+
+    class GradNet(nn.Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.net = net
+
+        def construct(self, *inputs):
+            return grad_all(self.net)(*inputs)
+
+    x = Tensor([11], mstype.int32)
+    y = Tensor([7], mstype.int32)
+
+    # graph mode
+    context.set_context(mode=context.GRAPH_MODE)
+    for_after_for_in_for_net = ForAfterForInForNet()
+    net = GradNet(for_after_for_in_for_net)
+    graph_forward_res = for_after_for_in_for_net(x, y)
+    graph_backward_res = net(x, y)
+
+    # pynative mode
+    context.set_context(mode=context.PYNATIVE_MODE)
+    for_after_for_in_for_net = ForAfterForInForNet()
+    net = GradNet(for_after_for_in_for_net)
+    pynative_forward_res = for_after_for_in_for_net(x, y)
+    pynative_backward_res = net(x, y)
+
+    assert graph_forward_res == pynative_forward_res
+    assert graph_backward_res == pynative_backward_res