!48178 add sequence case

Merge pull request !48178 from hujiahui8/tuple

tests/st/ops/dynamic_sequence/test_dynamic_sequence_count.py

@@ -0,0 +1,100 @@
+# 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.
+# ============================================================================
+import pytest
+
+import mindspore.nn as nn
+from mindspore.ops.operations import _sequence_ops as seq
+from mindspore import context
+from mindspore.common import mutable
+from mindspore.ops.composite import GradOperation
+
+context.set_context(mode=context.GRAPH_MODE)
+
+
+class Net(nn.Cell):
+    def __init__(self):
+        super().__init__()
+        self.seq_count = seq.SequenceCount()
+
+    def construct(self, x, y):
+        return self.seq_count(x, y)
+
+
+@pytest.mark.level1
+@pytest.mark.platform_x86_gpu
+@pytest.mark.env_onecard
+def test_seq_count_tuple_dy():
+    """
+    Feature: test sequence_count op
+    Description: first input is dynamic sequence
+    Expectation: the result match with tuple result
+    """
+    x = mutable((1, 2, 3), True)
+    y = 3
+    expect = 1
+    net = Net()
+    res = net(x, y)
+    assert res == expect
+
+
+@pytest.mark.level1
+@pytest.mark.platform_x86_gpu
+@pytest.mark.env_onecard
+def test_seq_count_scalar_dy():
+    """
+    Feature: test sequence_count op
+    Description: second input is dynamic scalar
+    Expectation: the result match with tuple result
+    """
+    x = (0, 1, 1, 2)
+    y = mutable(1)
+    expect = 2
+    net = Net()
+    res = net(x, y)
+    assert res == expect
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu
+@pytest.mark.env_onecard
+def test_seq_count_all_dy():
+    """
+    Feature: test sequence_count op
+    Description: two inputs are dynamic sequence
+    Expectation: the result match with tuple result
+    """
+    x = mutable((1, 2, 3, 3, 2, 3), True)
+    y = mutable(3)
+    expect = 3
+    net = Net()
+    res = net(x, y)
+    assert res == expect
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu
+@pytest.mark.env_onecard
+def test_seq_count_grad():
+    """
+    Feature: test sequence_count grad op
+    Description: two inputs are dynamic sequence
+    Expectation: the result match with tuple result
+    """
+    x = mutable((1, 2, 3), True)
+    y = mutable(2)
+    dout = mutable(2)
+    net = Net()
+    grad_func = GradOperation(get_all=True, sens_param=True)(net)
+    grad_func(x, y, dout)

tests/st/ops/dynamic_sequence/test_dynamic_sequence_index.py

@@ -0,0 +1,100 @@
+# 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.
+# ============================================================================
+import pytest
+
+import mindspore.nn as nn
+from mindspore.ops.operations import _sequence_ops as seq
+from mindspore import context
+from mindspore.common import mutable
+from mindspore.ops.composite import GradOperation
+
+context.set_context(mode=context.GRAPH_MODE)
+
+
+class Net(nn.Cell):
+    def __init__(self):
+        super().__init__()
+        self.seq_index = seq.SequenceIndex()
+
+    def construct(self, x, y):
+        return self.seq_index(x, y)
+
+
+@pytest.mark.level1
+@pytest.mark.platform_x86_gpu
+@pytest.mark.env_onecard
+def test_seq_index_tuple_dy():
+    """
+    Feature: test sequence_index op
+    Description: first input is dynamic sequence
+    Expectation: the result match with tuple result
+    """
+    x = mutable((1, 2, 3), True)
+    y = 3
+    expect = 2
+    net = Net()
+    res = net(x, y)
+    assert res == expect
+
+
+@pytest.mark.level1
+@pytest.mark.platform_x86_gpu
+@pytest.mark.env_onecard
+def test_seq_index_scalar_dy():
+    """
+    Feature: test sequence_index op
+    Description: second input is dynamic scalar
+    Expectation: the result match with tuple result
+    """
+    x = (0, 1, 1, 2)
+    y = mutable(1)
+    expect = 1
+    net = Net()
+    res = net(x, y)
+    assert res == expect
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu
+@pytest.mark.env_onecard
+def test_seq_index_all_dy():
+    """
+    Feature: test sequence_index op
+    Description: two inputs are dynamic sequence
+    Expectation: the result match with tuple result
+    """
+    x = mutable((1, 2, 3, 3, 2, 3), True)
+    y = mutable(3)
+    expect = 2
+    net = Net()
+    res = net(x, y)
+    assert res == expect
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu
+@pytest.mark.env_onecard
+def test_seq_index_grad():
+    """
+    Feature: test sequence_index grad op
+    Description: two inputs are dynamic sequence
+    Expectation: the result match with tuple result
+    """
+    x = mutable((1, 2, 3), True)
+    y = mutable(2)
+    dout = mutable(2)
+    net = Net()
+    grad_func = GradOperation(get_all=True, sens_param=True)(net)
+    grad_func(x, y, dout)

tests/st/ops/dynamic_sequence/test_dynamic_sequence_make_tuple.py

@@ -0,0 +1,106 @@
+# 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.
+# ============================================================================
+import pytest
+
+import mindspore.nn as nn
+from mindspore.ops.operations import _sequence_ops as seq
+from mindspore import context
+from mindspore.common import mutable
+from mindspore.ops.composite import GradOperation
+
+context.set_context(mode=context.GRAPH_MODE)
+
+
+@pytest.mark.level1
+@pytest.mark.platform_x86_gpu
+@pytest.mark.env_onecard
+def test_real_make_tuple():
+    """
+    Feature: test real_make_tuple op
+    Description: all inputs are scalar
+    Expectation: the result match with tuple result
+    """
+    class Net(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.func = seq.SequenceMul()
+
+        def construct(self, *x):
+            make_tuple = (x[0], x[1], x[2])
+            return self.func(make_tuple, x[3])
+
+    x_0 = 1
+    x_1 = 2
+    x_2 = 3
+    x_3 = mutable(2)
+    expect = (1, 2, 3, 1, 2, 3)
+    net = Net()
+    res = net(x_0, x_1, x_2, x_3)
+    assert res == expect
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu
+@pytest.mark.env_onecard
+def test_real_make_tuple_dy():
+    """
+    Feature: test real_make_tuple op
+    Description: all inputs are dynamic scalar
+    Expectation: the result match with tuple result
+    """
+    class Net(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.func = seq.SequenceMul()
+
+        def construct(self, *x):
+            make_tuple = (x[0], x[1])
+            return self.func(make_tuple, x[2])
+
+    x_0 = mutable(0)
+    x_1 = mutable(2)
+    x_3 = mutable(2)
+    expect = (0, 2, 0, 2)
+    net = Net()
+    res = net(x_0, x_1, x_3)
+    assert res == expect
+
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu
+@pytest.mark.env_onecard
+def test_real_make_tuple_grad():
+    """
+    Feature: test real_make_tuple grad op
+    Description: all inputs are dynamic scalar
+    Expectation: the result match with tuple result
+    """
+    class Net(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.func = seq.SequenceMul()
+
+        def construct(self, *x):
+            make_tuple = (x[0], x[1])
+            return self.func(make_tuple, x[2])
+
+    x_0 = mutable(0)
+    x_1 = mutable(2)
+    x_2 = mutable(2)
+    dout = mutable((0, 2, 0, 2), True)
+    net = Net()
+    grad_func = GradOperation(get_all=True, sens_param=True)(net)
+    grad_func(x_0, x_1, x_2, dout)

tests/st/ops/dynamic_sequence/test_dynamic_sequence_mul.py

@@ -0,0 +1,100 @@
+# 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.
+# ============================================================================
+import pytest
+
+import mindspore.nn as nn
+from mindspore.ops.operations import _sequence_ops as seq
+from mindspore import context
+from mindspore.common import mutable
+from mindspore.ops.composite import GradOperation
+
+context.set_context(mode=context.GRAPH_MODE)
+
+
+class Net(nn.Cell):
+    def __init__(self):
+        super().__init__()
+        self.seq_mul = seq.SequenceMul()
+
+    def construct(self, x, y):
+        return self.seq_mul(x, y)
+
+
+@pytest.mark.level1
+@pytest.mark.platform_x86_gpu
+@pytest.mark.env_onecard
+def test_seq_mul_tuple_dy():
+    """
+    Feature: test sequence_mul op
+    Description: first input is dynamic sequence
+    Expectation: the result match with tuple result
+    """
+    x = mutable((1, 2, 3), True)
+    y = 2
+    expect = (1, 2, 3, 1, 2, 3)
+    net = Net()
+    res = net(x, y)
+    assert res == expect
+
+
+@pytest.mark.level1
+@pytest.mark.platform_x86_gpu
+@pytest.mark.env_onecard
+def test_seq_mul_scalar_dy():
+    """
+    Feature: test sequence_mul op
+    Description: second input is dynamic scalar
+    Expectation: the result match with tuple result
+    """
+    x = (0, 1, 1, 2)
+    y = mutable(1)
+    expect = (0, 1, 1, 2)
+    net = Net()
+    res = net(x, y)
+    assert res == expect
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu
+@pytest.mark.env_onecard
+def test_seq_mul_all_dy():
+    """
+    Feature: test sequence_mul op
+    Description: two inputs are dynamic sequence
+    Expectation: the result match with tuple result
+    """
+    x = mutable((1, 2, 3), True)
+    y = mutable(3)
+    expect = (1, 2, 3, 1, 2, 3, 1, 2, 3)
+    net = Net()
+    res = net(x, y)
+    assert res == expect
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu
+@pytest.mark.env_onecard
+def test_seq_mul_grad():
+    """
+    Feature: test sequence_mul grad op
+    Description: two inputs are dynamic sequence
+    Expectation: the result match with tuple result
+    """
+    x = mutable((1, 2, 3), True)
+    y = mutable(2)
+    dout = mutable((4, 5, 6, 7, 8, 9), True)
+    net = Net()
+    grad_func = GradOperation(get_all=True, sens_param=True)(net)
+    grad_func(x, y, dout)