!48358 add sequence test

Merge pull request !48358 from NaCN/test_seq

mindspore/ccsrc/plugin/device/cpu/kernel/sequence/scalar_arithmetic_cpu_kernel.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020-2022 Huawei Technologies Co., Ltd
+ * 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.

mindspore/ccsrc/plugin/device/cpu/kernel/sequence/scalar_arithmetic_cpu_kernel.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2022 Huawei Technologies Co., Ltd
+ * 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.

mindspore/ccsrc/plugin/device/cpu/kernel/sequence/scalar_bitwise_cpu_kernel.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020-2022 Huawei Technologies Co., Ltd
+ * 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.

mindspore/ccsrc/plugin/device/cpu/kernel/sequence/scalar_bitwise_cpu_kernel.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2022 Huawei Technologies Co., Ltd
+ * 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.

mindspore/ccsrc/plugin/device/cpu/kernel/sequence/scalar_bool_cpu_kernel.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020-2022 Huawei Technologies Co., Ltd
+ * 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.

mindspore/ccsrc/plugin/device/cpu/kernel/sequence/scalar_bool_cpu_kernel.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2022 Huawei Technologies Co., Ltd
+ * 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.

mindspore/ccsrc/plugin/device/cpu/kernel/sequence/sequence_add_cpu_kernel.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2022 Huawei Technologies Co., Ltd
+ * 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.

mindspore/ccsrc/plugin/device/cpu/kernel/sequence/sequence_add_cpu_kernel.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2022 Huawei Technologies Co., Ltd
+ * 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.

mindspore/ccsrc/plugin/device/cpu/kernel/sequence/sequence_add_offset_cpu_kernel.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2022 Huawei Technologies Co., Ltd
+ * 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.
@@ -76,25 +76,20 @@ bool SequenceAddOffsetCpuKernelMod::Launch(const std::vector<KernelTensorPtr> &i
 
 std::vector<std::pair<KernelAttr, SequenceAddOffsetCpuKernelMod::SequenceAddOffsetFunc>>
   SequenceAddOffsetCpuKernelMod::func_list_ = {{KernelAttr()
+                                                  .AddInputAttr(kObjectTypeTuple, kNumberTypeFloat32)
+                                                  .AddInputAttr(kObjectTypeTuple, kNumberTypeFloat32)
+                                                  .AddOutputAttr(kObjectTypeTuple, kNumberTypeInt64),
+                                                &SequenceAddOffsetCpuKernelMod::LaunchKernel<float>},
+                                               {KernelAttr()
                                                   .AddInputAttr(kObjectTypeTuple, kNumberTypeFloat64)
                                                   .AddInputAttr(kObjectTypeTuple, kNumberTypeFloat64)
                                                   .AddOutputAttr(kObjectTypeTuple, kNumberTypeInt64),
                                                 &SequenceAddOffsetCpuKernelMod::LaunchKernel<double>},
-                                               {KernelAttr()
-                                                  .AddInputAttr(kObjectTypeList, kNumberTypeInt32)
-                                                  .AddInputAttr(kObjectTypeList, kNumberTypeInt32)
-                                                  .AddOutputAttr(kObjectTypeTuple, kNumberTypeInt64),
-                                                &SequenceAddOffsetCpuKernelMod::LaunchKernel<int>},
                                                {KernelAttr()
                                                   .AddInputAttr(kObjectTypeTuple, kNumberTypeInt32)
                                                   .AddInputAttr(kObjectTypeTuple, kNumberTypeInt32)
                                                   .AddOutputAttr(kObjectTypeTuple, kNumberTypeInt64),
                                                 &SequenceAddOffsetCpuKernelMod::LaunchKernel<int>},
-                                               {KernelAttr()
-                                                  .AddInputAttr(kObjectTypeList, kNumberTypeInt64)
-                                                  .AddInputAttr(kObjectTypeList, kNumberTypeInt64)
-                                                  .AddOutputAttr(kObjectTypeTuple, kNumberTypeInt64),
-                                                &SequenceAddOffsetCpuKernelMod::LaunchKernel<int64_t>},
                                                {KernelAttr()
                                                   .AddInputAttr(kObjectTypeTuple, kNumberTypeInt64)
                                                   .AddInputAttr(kObjectTypeTuple, kNumberTypeInt64)

mindspore/ccsrc/plugin/device/cpu/kernel/sequence/sequence_getitem_cpu_kernel.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020-2022 Huawei Technologies Co., Ltd
+ * 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.

mindspore/ccsrc/plugin/device/cpu/kernel/sequence/sequence_getitem_cpu_kernel.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020-2022 Huawei Technologies Co., Ltd
+ * 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.

mindspore/ccsrc/plugin/device/cpu/kernel/sequence/sequence_len_cpu_kernel.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020-2022 Huawei Technologies Co., Ltd
+ * 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.

mindspore/ccsrc/plugin/device/cpu/kernel/sequence/sequence_len_cpu_kernel.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020-2022 Huawei Technologies Co., Ltd
+ * 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.

mindspore/ccsrc/plugin/device/cpu/kernel/sequence/sequence_make_range_cpu_kernel.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020-2022 Huawei Technologies Co., Ltd
+ * 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.
@@ -36,7 +36,7 @@ T Sign(T num) {
   } else {
     return static_cast<T>(-1.0);
   }
-}  // namespace
+}
 }  // namespace
 
 bool MakeRangeCpuKernelMod::Init(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs,
@@ -74,7 +74,7 @@ bool MakeRangeCpuKernelMod::LaunchKernel(const std::vector<KernelTensorPtr> &inp
     }
   }
   return true;
-}  // namespace kernel
+}
 
 const std::vector<std::pair<KernelAttr, MakeRangeCpuKernelMod::KernelRunFunc>> &MakeRangeCpuKernelMod::GetFuncList()
   const {

mindspore/ccsrc/plugin/device/cpu/kernel/sequence/sequence_make_range_cpu_kernel.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020-2022 Huawei Technologies Co., Ltd
+ * 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.

mindspore/ccsrc/plugin/device/cpu/kernel/sequence/sequence_setitem_cpu_kernel.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2022 Huawei Technologies Co., Ltd
+ * 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.

mindspore/ccsrc/plugin/device/cpu/kernel/sequence/sequence_setitem_cpu_kernel.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2022 Huawei Technologies Co., Ltd
+ * 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.

mindspore/ccsrc/plugin/device/cpu/kernel/sequence/sequence_slice_cpu_kernel.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2022 Huawei Technologies Co., Ltd
+ * 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.

mindspore/ccsrc/plugin/device/cpu/kernel/sequence/sequence_slice_grad_cpu_kernel.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2022 Huawei Technologies Co., Ltd
+ * 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.

mindspore/ccsrc/plugin/device/cpu/kernel/sequence/sequence_to_tensor_cpu_kernel.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020-2022 Huawei Technologies Co., Ltd
+ * 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.

mindspore/ccsrc/plugin/device/cpu/kernel/sequence/sequence_to_tensor_cpu_kernel.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020-2022 Huawei Technologies Co., Ltd
+ * 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.

mindspore/ccsrc/plugin/device/cpu/kernel/sequence/sequence_zeros_like_cpu_kernel.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2022 Huawei Technologies Co., Ltd
+ * 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.

mindspore/ccsrc/plugin/device/cpu/kernel/sequence/sequence_zeros_like_cpu_kernel.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2022 Huawei Technologies Co., Ltd
+ * 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.

mindspore/ccsrc/plugin/device/cpu/kernel/sequence/tensor_to_sequence_cpu_kernel.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020-2022 Huawei Technologies Co., Ltd
+ * 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.

mindspore/ccsrc/plugin/device/cpu/kernel/sequence/tensor_to_sequence_cpu_kernel.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020-2022 Huawei Technologies Co., Ltd
+ * 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.

mindspore/python/mindspore/ops/composite/multitype_ops/zeros_like_impl.py

@@ -18,6 +18,7 @@
 from __future__ import absolute_import
 from mindspore.ops.composite import base
 from mindspore.ops import functional as F
+from mindspore.ops.operations import _sequence_ops as seq
 
 zeros_like_leaf = base.MultitypeFuncGraph('zeros_like_leaf', True)
 """
@@ -59,6 +60,12 @@ def _zeros_like_tensor(x):
     return F.zeros_like(x)
 
 
+@zeros_like_leaf.register("Tuple")
+def _zeros_like_tuple(x):
+    """Returns a Tuple with the same shape and dtype as x and all elements are 0."""
+    return seq.SequenceZerosLike()(x)
+
+
 @zeros_like_leaf.register("COOTensor")
 def _zeros_like_coo_tensor(x):
     """Returns a tensor with the same shape and dtype as x and all elements are 0."""

tests/st/ops/dynamic_sequence/test_dynamic_sequence_add.py

@@ -0,0 +1,118 @@
+# 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.nn import Cell
+from mindspore.ops.composite import GradOperation
+
+context.set_context(mode=context.GRAPH_MODE)
+
+
+class NetAdd(nn.Cell):
+    def __init__(self):
+        super().__init__()
+        self.seq_add = seq.SequenceAdd()
+
+    def construct(self, x, y):
+        return self.seq_add(x, y)
+
+
+class NetAddOffset(nn.Cell):
+    def __init__(self):
+        super().__init__()
+        self.seq_add_offset = seq.SequenceAddOffset()
+
+    def construct(self, x, y):
+        return self.seq_add_offset(x, y)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_seq_add():
+    """
+    Feature: test sequence_add op
+    Description: two inputs are dynamic sequence
+    Expectation: the result match with tuple result
+    """
+    x = mutable((1, 2, 3), True)
+    y = mutable((4, 5, 6), True)
+    expect = (1, 2, 3, 4, 5, 6)
+    net = NetAdd()
+    res = net(x, y)
+    assert res == expect
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_seq_add_offset():
+    """
+    Feature: test sequence_add_offset op
+    Description: inputs are dynamic sequence.
+    Expectation: the result match with tuple result
+    """
+    x = mutable((1, 2, 3), True)
+    y = mutable((4, 5, 6), True)
+    expect = (0, 3)
+    net = NetAddOffset()
+    res = net(x, y)
+    assert res == expect
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_seq_add_grad():
+    """
+    Feature: test sequence add grad op
+    Description: inputs are dynamic sequence.
+    Expectation: the result match with tuple result
+    """
+    class Net(Cell):
+        def construct(self, x, y):
+            return x + y
+
+    net_ms = Net()
+    input_x = mutable((1, 2, 3), True)
+    input_y = mutable((3, 4, 5, 6), True)
+    dout = mutable((1, 1, 1, 1, 1, 1, 1), True)
+    grad_func = GradOperation(get_all=True, sens_param=True)(net_ms)
+    print("grad out = ", grad_func(input_x, input_y, dout))
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_seq_add_grad_other():
+    """
+    Feature: test sequence add grad op
+    Description: inputs are dynamic sequence.
+    Expectation: the result match with tuple result
+    """
+    class Net(Cell):
+        def construct(self, x, y):
+            return x + y
+
+    net_ms = Net()
+    input_x = mutable((1, 2, 3), True)
+    input_y = (3, 4, 5, 6)
+    dout = mutable((1, 1, 1, 1, 1, 1, 1), True)
+    grad_func = GradOperation(get_all=True, sens_param=True)(net_ms)
+    print("grad out1 = ", grad_func(input_x, input_y, dout))

tests/st/ops/dynamic_sequence/test_dynamic_sequence_getitem.py

@@ -0,0 +1,61 @@
+# 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 import context
+from mindspore.common import mutable
+from mindspore.ops.composite import GradOperation
+
+context.set_context(mode=context.GRAPH_MODE)
+
+
+class NetGetItem(nn.Cell):
+    def construct(self, seq, idx):
+        return seq[idx]
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_seq_getitem():
+    """
+    Feature: test sequence getitem op
+    Description: setitem operation on tuple type
+    Expectation: the behavior is matched to python style
+    """
+    seq = mutable((1, 2, 3, 4, 5, 6), True)
+    idx = 3
+    expect = 4
+    net = NetGetItem()
+    res = net(seq, idx)
+    assert res == expect
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_seq_getitem_grad():
+    """
+    Feature: test sequence getitem grad op
+    Description: setitem operation on tuple type
+    Expectation: the behavior is matched to python style
+    """
+    net_ms = NetGetItem()
+    seq = mutable((1, 2, 3, 4, 5, 6), True)
+    index = 1
+    dout = 1
+    grad_func = GradOperation(get_all=True, sens_param=True)(net_ms)
+    print("grad out1 = ", grad_func(seq, index, dout))

tests/st/ops/dynamic_sequence/test_dynamic_sequence_make_range.py

@@ -0,0 +1,71 @@
+# 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
+from mindspore.ops.operations import _sequence_ops as seq
+from mindspore import context
+from mindspore.nn import Cell
+from mindspore.common import mutable
+from mindspore.ops.composite import GradOperation
+from tuple_help import TupleFactory
+
+context.set_context(mode=context.GRAPH_MODE)
+
+
+class Net(Cell):
+    def __init__(self):
+        super().__init__()
+        self.func = seq.make_range()
+
+    def construct(self, x, y, z):
+        return self.func(x, y, z)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_seqence_make_range():
+    """
+    Feature: test sequence makerange op
+    Description: setitem operation on tuple type
+    Expectation: the behavior is matched to python style
+    """
+
+    def func(x, y, z):
+        return tuple(range(x, y, z))
+
+    net_ms = Net()
+    input_x = 1
+    input_y = 1000
+    input_z = 31
+    fact = TupleFactory(net_ms, func, (input_x, input_y, input_z))
+    fact.forward_cmp()
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_seqence_make_range_grad():
+    """
+    Feature: test sequence makerange grad
+    Description: setitem operation on tuple type
+    Expectation: the behavior is matched to python style
+    """
+    net_ms = Net()
+    input_x = mutable(10)
+    input_y = mutable(100)
+    input_z = mutable(3)
+    dout = mutable((1, 1), True)
+    grad_func = GradOperation(get_all=True, sens_param=True)(net_ms)
+    print("grad out1 = ", grad_func(input_x, input_y, input_z, dout))

tests/st/ops/dynamic_sequence/test_dynamic_sequence_setitem.py

@@ -0,0 +1,82 @@
+# 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 import functional as F
+from mindspore import context
+from mindspore.common import mutable
+from mindspore.ops.composite import GradOperation
+
+context.set_context(mode=context.GRAPH_MODE)
+
+
+class NetSetItem(nn.Cell):
+    def construct(self, seq, idx, value):
+        return F.tuple_setitem(seq, idx, value)
+
+
+@pytest.mark.level1
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_seq_setitem():
+    """
+    Feature: test sequence_setitem op
+    Description: setitem operation on tuple type
+    Expectation: the behavior is matched to python style
+    """
+    seq = mutable((1, 2, 3, 4, 5, 6), True)
+    value = 9
+    idx = 3
+    expect = (1, 2, 3, 9, 5, 6)
+    net = NetSetItem()
+    res = net(seq, idx, value)
+    assert res == expect
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_seq_setitem_grad_0():
+    """
+    Feature: test sequence setitem grad op
+    Description: setitem operation on tuple type
+    Expectation: the behavior is matched to python style
+    """
+    net_ms = NetSetItem()
+    input_x = mutable((1, 2, 3), True)
+    idx = mutable(1)
+    value = mutable(8)
+    dout = mutable((1, 1, 1), True)
+    grad_func = GradOperation(get_all=True, sens_param=True)(net_ms)
+    print("grad out0 = ", grad_func(input_x, idx, value, dout))
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_seq_setitem_grad_1():
+    """
+    Feature: test sequence setitem grad op
+    Description: setitem operation on tuple type
+    Expectation: the behavior is matched to python style
+    """
+    net_ms = NetSetItem()
+    input_x = mutable((1, 2, 3), True)
+    idx = 1
+    value = 8
+    dout = mutable((1, 1, 1), True)
+    grad_func = GradOperation(get_all=True, sens_param=True)(net_ms)
+    print("grad out1 = ", grad_func(input_x, idx, value, dout))

tests/st/ops/dynamic_sequence/test_dynamic_sequence_slice.py

@@ -0,0 +1,79 @@
+# 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 import context
+from mindspore.ops.operations import _sequence_ops as S
+from mindspore.common import mutable
+from mindspore.ops.composite import GradOperation
+from tuple_help import TupleFactory
+
+context.set_context(mode=context.GRAPH_MODE)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_seq_slice():
+    """
+    Feature: test sequence_slice op
+    Description: slice operation on tuple type
+    Expectation: the behavior is matched to python style
+    """
+    class Net(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.seq_slice = S.SequenceSlice()
+
+        def construct(self, seq, start, stop, step):
+            return self.seq_slice(seq, start, stop, step)
+
+    def func(seq, start, stop, step):
+        return seq[start:stop:step]
+
+    seq = (1, 2, 3, 4, 5, 6)
+    start = 1
+    stop = 3
+    step = 1
+    net_ms = Net()
+    fact = TupleFactory(net_ms, func, (seq, start, stop, step))
+    fact.forward_cmp()
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_seq_slice_grad():
+    """
+    Feature: test sequence_slice grad
+    Description: slice operation on tuple type
+    Expectation: the behavior is matched to python style
+    """
+    class Net(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.seq_slice = S.SequenceSlice()
+
+        def construct(self, seq, start, stop, step):
+            return self.seq_slice(seq, start, stop, step)
+
+    seq = mutable((1, 2, 3, 4, 5, 6), True)
+    start = 1
+    stop = 3
+    step = 1
+    dout = mutable((1, 1), True)
+    net_ms = Net()
+    grad_func = GradOperation(get_all=True, sens_param=True)(net_ms)
+    print("grad out1 = ", grad_func(seq, start, stop, step, dout))

tests/st/ops/dynamic_sequence/test_dynamic_sequence_zeros_like.py

@@ -0,0 +1,47 @@
+# 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 numpy as np
+from mindspore.ops.operations import _sequence_ops as seq
+from mindspore import context
+from mindspore.nn import Cell
+from tuple_help import TupleFactory
+
+context.set_context(mode=context.GRAPH_MODE)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_seqence_zeros_like():
+    """
+    Feature: test sequence zeroslike op
+    Description: setitem operation on tuple type
+    Expectation: the behavior is matched to python style
+    """
+    class Net(Cell):
+        def __init__(self):
+            super().__init__()
+            self.func = seq.SequenceZerosLike()
+
+        def construct(self, x):
+            return self.func(x)
+
+    def func(x):
+        return tuple(np.zeros_like(x))
+    net_ms = Net()
+    input_x = (1, 2, 3, 4, 5)
+    fact = TupleFactory(net_ms, func, (input_x,))
+    fact.forward_cmp()

tests/st/ops/dynamic_sequence/test_shape_sequence.py

@@ -0,0 +1,49 @@
+# 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 numpy as np
+from mindspore import context
+from mindspore import Tensor
+from mindspore.nn import Cell
+import mindspore
+
+context.set_context(mode=context.GRAPH_MODE)
+
+
+class ShapeNet(Cell):
+    def __init__(self):
+        super().__init__()
+        self.shape = mindspore.ops.shape
+
+    def construct(self, x):
+        return self.shape(x)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_shape():
+    """
+    Feature: test sequence shape op
+    Description: setitem operation on tuple type
+    Expectation: the behavior is matched to python style
+    """
+    x = Tensor(np.random.randn(1, 2, 4).astype(np.float32))
+    dynx = Tensor(shape=(1, 2, None), dtype=mindspore.float32)
+    expect_x = (1, 2, 4)
+    net = ShapeNet()
+    net.set_inputs(dynx)
+    res_x = net(x)
+    assert expect_x == res_x