!14997 add scatter operations: ScatterAdd ScatterSub ScatterMul ScatterDiv ScatterMax ScatterMin ScatterUpdate

From: @dinglongwei
Reviewed-by: @wuxuejian,@liangchenghui
Signed-off-by: @wuxuejian

mindspore/ccsrc/backend/kernel_compiler/cpu/scatter_arithmetic_cpu_kernel.cc

@@ -0,0 +1,200 @@
+/**
+ * Copyright 2021 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.
+ */
+#include "backend/kernel_compiler/cpu/scatter_arithmetic_cpu_kernel.h"
+#include <map>
+#include <limits>
+#include "runtime/device/cpu/cpu_device_address.h"
+
+namespace mindspore {
+namespace kernel {
+
+template <typename T>
+void ScatterArithmeticCPUKernel<T>::InitKernel(const CNodePtr &kernel_node) {
+  CheckParam(kernel_node);
+  kernel_name_ = AnfAlgo::GetCNodeName(kernel_node);
+  auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+  input_size_ = 1;
+  inner_size_ = 1;
+  for (size_t i = 1; i < input_shape.size(); i++) {
+    inner_size_ *= input_shape[i];
+  }
+  input_size_ = input_shape[0] * inner_size_;
+  auto indices_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
+  indices_size_ = 1;
+  for (size_t i = 0; i < indices_shape.size(); i++) {
+    indices_size_ *= indices_shape[i];
+  }
+}
+
+template <typename T>
+void ScatterArithmeticCPUKernel<T>::CheckParam(const CNodePtr &kernel_node) const {
+  MS_EXCEPTION_IF_NULL(kernel_node);
+  size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+  if (input_num != 3) {
+    MS_LOG(EXCEPTION) << "Input number is " << input_num << ", but ScatterAdd needs 3 inputs.";
+  }
+  size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
+  if (output_num != 1) {
+    MS_LOG(EXCEPTION) << "Output number is " << output_num << ", but ScatterAdd has 1 output.";
+  }
+}
+
+template <typename T>
+bool ScatterArithmeticCPUKernel<T>::Launch(const std::vector<kernel::AddressPtr> &inputs,
+                                           const std::vector<kernel::AddressPtr> &workspace,
+                                           const std::vector<kernel::AddressPtr> &outputs) {
+  static const std::map<std::string, std::function<void(ScatterArithmeticCPUKernel *, T *, const int *, const T *)>>
+    kScatterArithmeticBinOpFuncMap{{"ScatterAdd", &ScatterArithmeticCPUKernel<T>::ScatterAdd},
+                                   {"ScatterSub", &ScatterArithmeticCPUKernel<T>::ScatterSub},
+                                   {"ScatterMul", &ScatterArithmeticCPUKernel<T>::ScatterMul},
+                                   {"ScatterDiv", &ScatterArithmeticCPUKernel<T>::ScatterDiv},
+                                   {"ScatterMax", &ScatterArithmeticCPUKernel<T>::ScatterMax},
+                                   {"ScatterMin", &ScatterArithmeticCPUKernel<T>::ScatterMin},
+                                   {"ScatterUpdate", &ScatterArithmeticCPUKernel<T>::ScatterUpdate}};
+  if (kScatterArithmeticBinOpFuncMap.find(kernel_name_) != kScatterArithmeticBinOpFuncMap.end()) {
+    T *input = reinterpret_cast<T *>(inputs[0]->addr);
+    int *indices = reinterpret_cast<int *>(inputs[1]->addr);
+    T *updates = reinterpret_cast<T *>(inputs[2]->addr);
+    T *output = reinterpret_cast<T *>(outputs[0]->addr);
+    kScatterArithmeticBinOpFuncMap.at(kernel_name_)(this, input, indices, updates);
+    auto bufferSize = outputs[0]->size;
+    auto ret = memcpy_s(output, bufferSize, input, input_size_ * sizeof(T));
+    if (ret != EOK) {
+      MS_LOG(EXCEPTION) << "Memory copy failed!";
+    }
+  } else {
+    MS_LOG(EXCEPTION) << "Not support operator:" << kernel_name_;
+  }
+  return true;
+}
+
+template <typename T>
+void ScatterArithmeticCPUKernel<T>::ScatterAdd(T *input, const int *indices, const T *updates) {
+  auto task = [this, input, indices, updates](size_t start, size_t end) {
+    for (size_t i = start; i < end; i++) {
+      auto base_index_updates = i * inner_size_;
+      auto base_index_input = indices[i] * inner_size_;
+      for (size_t j = 0; j < inner_size_; j++) {
+        input[base_index_input + j] += updates[base_index_updates + j];
+      }
+    }
+  };
+  CPUKernelUtils::ParallelFor(task, indices_size_);
+}
+
+template <typename T>
+void ScatterArithmeticCPUKernel<T>::ScatterSub(T *input, const int *indices, const T *updates) {
+  auto task = [this, input, indices, updates](size_t start, size_t end) {
+    for (size_t i = start; i < end; i++) {
+      auto base_index_updates = i * inner_size_;
+      auto base_index_input = indices[i] * inner_size_;
+      for (size_t j = 0; j < inner_size_; j++) {
+        input[base_index_input + j] -= updates[base_index_updates + j];
+      }
+    }
+  };
+  CPUKernelUtils::ParallelFor(task, indices_size_);
+}
+
+template <typename T>
+void ScatterArithmeticCPUKernel<T>::ScatterMul(T *input, const int *indices, const T *updates) {
+  auto task = [this, input, indices, updates](size_t start, size_t end) {
+    for (size_t i = start; i < end; i++) {
+      auto base_index_updates = i * inner_size_;
+      auto base_index_input = indices[i] * inner_size_;
+      for (size_t j = 0; j < inner_size_; j++) {
+        input[base_index_input + j] *= updates[base_index_updates + j];
+      }
+    }
+  };
+  CPUKernelUtils::ParallelFor(task, indices_size_);
+}
+
+template <typename T>
+void ScatterArithmeticCPUKernel<T>::ScatterDiv(T *input, const int *indices, const T *updates) {
+  auto task = [this, input, indices, updates](size_t start, size_t end) {
+    for (size_t i = start; i < end; i++) {
+      for (size_t j = 0; j < inner_size_; j++) {
+        auto dividend = input[indices[i] * inner_size_ + j];
+        auto divisor = updates[i * inner_size_ + j];
+        if (divisor == 0) {
+          if (dividend == 0) {
+            input[indices[i] * inner_size_ + j] = std::numeric_limits<T>::quiet_NaN();
+            continue;
+          }
+          if (std::numeric_limits<T>::has_infinity) {
+            input[indices[i] * inner_size_ + j] =
+              dividend > 0 ? std::numeric_limits<T>::infinity() : -std::numeric_limits<T>::infinity();
+          } else {
+            input[indices[i] * inner_size_ + j] =
+              dividend > 0 ? std::numeric_limits<T>::max() : std::numeric_limits<T>::min();
+          }
+          continue;
+        }
+        input[indices[i] * inner_size_ + j] = dividend / divisor;
+      }
+    }
+  };
+  CPUKernelUtils::ParallelFor(task, indices_size_);
+}
+
+template <typename T>
+void ScatterArithmeticCPUKernel<T>::ScatterMax(T *input, const int *indices, const T *updates) {
+  auto task = [this, input, indices, updates](size_t start, size_t end) {
+    for (size_t i = start; i < end; i++) {
+      auto base_index_updates = i * inner_size_;
+      auto base_index_input = indices[i] * inner_size_;
+      for (size_t j = 0; j < inner_size_; j++) {
+        input[base_index_input + j] = input[base_index_input + j] > updates[base_index_updates + j]
+                                        ? input[base_index_input + j]
+                                        : updates[base_index_updates + j];
+      }
+    }
+  };
+  CPUKernelUtils::ParallelFor(task, indices_size_);
+}
+
+template <typename T>
+void ScatterArithmeticCPUKernel<T>::ScatterMin(T *input, const int *indices, const T *updates) {
+  auto task = [this, input, indices, updates](size_t start, size_t end) {
+    for (size_t i = start; i < end; i++) {
+      auto base_index_updates = i * inner_size_;
+      auto base_index_input = indices[i] * inner_size_;
+      for (size_t j = 0; j < inner_size_; j++) {
+        input[base_index_input + j] = input[base_index_input + j] < updates[base_index_updates + j]
+                                        ? input[base_index_input + j]
+                                        : updates[base_index_updates + j];
+      }
+    }
+  };
+  CPUKernelUtils::ParallelFor(task, indices_size_);
+}
+
+template <typename T>
+void ScatterArithmeticCPUKernel<T>::ScatterUpdate(T *input, const int *indices, const T *updates) {
+  auto task = [this, input, indices, updates](size_t start, size_t end) {
+    for (size_t i = start; i < end; i++) {
+      auto base_index_updates = i * inner_size_;
+      auto base_index_input = indices[i] * inner_size_;
+      for (size_t j = 0; j < inner_size_; j++) {
+        input[base_index_input + j] = updates[base_index_updates + j];
+      }
+    }
+  };
+  CPUKernelUtils::ParallelFor(task, indices_size_);
+}
+}  // namespace kernel
+}  // namespace mindspore

mindspore/ccsrc/backend/kernel_compiler/cpu/scatter_arithmetic_cpu_kernel.h

@@ -0,0 +1,210 @@
+/**
+ * Copyright 2021 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.
+ */
+#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_SCATTER_ARITHMETIC_CPU_KERNEL_H_
+#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_SCATTER_ARITHMETIC_CPU_KERNEL_H_
+#include <vector>
+#include <string>
+#include "backend/kernel_compiler/cpu/cpu_kernel.h"
+#include "backend/kernel_compiler/cpu/cpu_kernel_factory.h"
+
+namespace mindspore {
+namespace kernel {
+template <typename T>
+class ScatterArithmeticCPUKernel : public CPUKernel {
+ public:
+  ScatterArithmeticCPUKernel() = default;
+
+  ~ScatterArithmeticCPUKernel() override = default;
+
+  void InitKernel(const CNodePtr &kernel_node) override;
+
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs) override;
+
+ private:
+  void CheckParam(const CNodePtr &kernel_node) const;
+
+  void ScatterAdd(T *input, const int *indices, const T *updates);
+
+  void ScatterSub(T *input, const int *indices, const T *updates);
+
+  void ScatterMul(T *input, const int *indices, const T *updates);
+
+  void ScatterDiv(T *input, const int *indices, const T *updates);
+
+  void ScatterMax(T *input, const int *indices, const T *updates);
+
+  void ScatterMin(T *input, const int *indices, const T *updates);
+
+  void ScatterUpdate(T *input, const int *indices, const T *updates);
+
+  size_t input_size_{0};
+  size_t inner_size_{0};
+  size_t indices_size_{0};
+  std::string kernel_name_;
+};
+
+MS_REG_CPU_KERNEL_T(ScatterAdd,
+                    KernelAttr()
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddOutputAttr(kNumberTypeInt32),
+                    ScatterArithmeticCPUKernel, int32_t);
+MS_REG_CPU_KERNEL_T(ScatterAdd,
+                    KernelAttr()
+                      .AddInputAttr(kNumberTypeFloat32)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeFloat32)
+                      .AddOutputAttr(kNumberTypeFloat32),
+                    ScatterArithmeticCPUKernel, float);
+MS_REG_CPU_KERNEL_T(ScatterAdd,
+                    KernelAttr()
+                      .AddInputAttr(kNumberTypeInt64)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeInt64)
+                      .AddOutputAttr(kNumberTypeInt64),
+                    ScatterArithmeticCPUKernel, int64_t);
+MS_REG_CPU_KERNEL_T(ScatterSub,
+                    KernelAttr()
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddOutputAttr(kNumberTypeInt32),
+                    ScatterArithmeticCPUKernel, int32_t);
+MS_REG_CPU_KERNEL_T(ScatterSub,
+                    KernelAttr()
+                      .AddInputAttr(kNumberTypeFloat32)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeFloat32)
+                      .AddOutputAttr(kNumberTypeFloat32),
+                    ScatterArithmeticCPUKernel, float);
+MS_REG_CPU_KERNEL_T(ScatterSub,
+                    KernelAttr()
+                      .AddInputAttr(kNumberTypeInt64)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeInt64)
+                      .AddOutputAttr(kNumberTypeInt64),
+                    ScatterArithmeticCPUKernel, int64_t);
+MS_REG_CPU_KERNEL_T(ScatterMul,
+                    KernelAttr()
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddOutputAttr(kNumberTypeInt32),
+                    ScatterArithmeticCPUKernel, int32_t);
+MS_REG_CPU_KERNEL_T(ScatterMul,
+                    KernelAttr()
+                      .AddInputAttr(kNumberTypeFloat32)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeFloat32)
+                      .AddOutputAttr(kNumberTypeFloat32),
+                    ScatterArithmeticCPUKernel, float);
+MS_REG_CPU_KERNEL_T(ScatterMul,
+                    KernelAttr()
+                      .AddInputAttr(kNumberTypeInt64)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeInt64)
+                      .AddOutputAttr(kNumberTypeInt64),
+                    ScatterArithmeticCPUKernel, int64_t);
+MS_REG_CPU_KERNEL_T(ScatterDiv,
+                    KernelAttr()
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddOutputAttr(kNumberTypeInt32),
+                    ScatterArithmeticCPUKernel, int32_t);
+MS_REG_CPU_KERNEL_T(ScatterDiv,
+                    KernelAttr()
+                      .AddInputAttr(kNumberTypeFloat32)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeFloat32)
+                      .AddOutputAttr(kNumberTypeFloat32),
+                    ScatterArithmeticCPUKernel, float);
+MS_REG_CPU_KERNEL_T(ScatterDiv,
+                    KernelAttr()
+                      .AddInputAttr(kNumberTypeInt64)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeInt64)
+                      .AddOutputAttr(kNumberTypeInt64),
+                    ScatterArithmeticCPUKernel, int64_t);
+MS_REG_CPU_KERNEL_T(ScatterMax,
+                    KernelAttr()
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddOutputAttr(kNumberTypeInt32),
+                    ScatterArithmeticCPUKernel, int32_t);
+MS_REG_CPU_KERNEL_T(ScatterMax,
+                    KernelAttr()
+                      .AddInputAttr(kNumberTypeFloat32)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeFloat32)
+                      .AddOutputAttr(kNumberTypeFloat32),
+                    ScatterArithmeticCPUKernel, float);
+MS_REG_CPU_KERNEL_T(ScatterMax,
+                    KernelAttr()
+                      .AddInputAttr(kNumberTypeInt64)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeInt64)
+                      .AddOutputAttr(kNumberTypeInt64),
+                    ScatterArithmeticCPUKernel, int64_t);
+MS_REG_CPU_KERNEL_T(ScatterMin,
+                    KernelAttr()
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddOutputAttr(kNumberTypeInt32),
+                    ScatterArithmeticCPUKernel, int32_t);
+MS_REG_CPU_KERNEL_T(ScatterMin,
+                    KernelAttr()
+                      .AddInputAttr(kNumberTypeFloat32)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeFloat32)
+                      .AddOutputAttr(kNumberTypeFloat32),
+                    ScatterArithmeticCPUKernel, float);
+MS_REG_CPU_KERNEL_T(ScatterMin,
+                    KernelAttr()
+                      .AddInputAttr(kNumberTypeInt64)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeInt64)
+                      .AddOutputAttr(kNumberTypeInt64),
+                    ScatterArithmeticCPUKernel, int64_t);
+MS_REG_CPU_KERNEL_T(ScatterUpdate,
+                    KernelAttr()
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddOutputAttr(kNumberTypeInt32),
+                    ScatterArithmeticCPUKernel, int32_t);
+MS_REG_CPU_KERNEL_T(ScatterUpdate,
+                    KernelAttr()
+                      .AddInputAttr(kNumberTypeFloat32)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeFloat32)
+                      .AddOutputAttr(kNumberTypeFloat32),
+                    ScatterArithmeticCPUKernel, float);
+MS_REG_CPU_KERNEL_T(ScatterUpdate,
+                    KernelAttr()
+                      .AddInputAttr(kNumberTypeInt64)
+                      .AddInputAttr(kNumberTypeInt32)
+                      .AddInputAttr(kNumberTypeInt64)
+                      .AddOutputAttr(kNumberTypeInt64),
+                    ScatterArithmeticCPUKernel, int64_t);
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_SCATTER_ARITHMETIC_CPU_KERNEL_H_

mindspore/ops/operations/array_ops.py

@@ -39,7 +39,6 @@ from ...common.parameter import Parameter
 from ...common.tensor import Tensor
 
 
-
 class _ScatterOp(PrimitiveWithInfer):
     """
     Defines Scatter operators
@@ -3508,7 +3507,7 @@ class ScatterUpdate(_ScatterOp_Dynamic):
         TypeError: If `use_locking` is not a bool.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> np_x = np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]])
@@ -3631,7 +3630,7 @@ class ScatterMax(_ScatterOp):
         TypeError: If `use_locking` is not a bool.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``CPU``
 
     Examples:
         >>> input_x = Parameter(Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]), mindspore.float32), name="input_x")
@@ -3680,7 +3679,7 @@ class ScatterMin(_ScatterOp):
         TypeError: If `use_locking` is not a bool.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``CPU``
 
     Examples:
         >>> input_x = Parameter(Tensor(np.array([[0.0, 1.0, 2.0], [0.0, 0.0, 0.0]]), mindspore.float32), name="input_x")
@@ -3728,7 +3727,7 @@ class ScatterAdd(_ScatterOp_Dynamic):
         TypeError: If `use_locking` is not a bool.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> input_x = Parameter(Tensor(np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]), mindspore.float32), name="x")
@@ -3784,7 +3783,7 @@ class ScatterSub(_ScatterOp):
         TypeError: If `use_locking` is not a bool.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``CPU``
 
     Examples:
         >>> input_x = Parameter(Tensor(np.array([[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]]), mindspore.float32), name="x")
@@ -3832,7 +3831,7 @@ class ScatterMul(_ScatterOp):
         TypeError: If `use_locking` is not a bool.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``CPU``
 
     Examples:
         >>> input_x = Parameter(Tensor(np.array([[1.0, 1.0, 1.0], [2.0, 2.0, 2.0]]), mindspore.float32), name="x")
@@ -3880,7 +3879,7 @@ class ScatterDiv(_ScatterOp):
         TypeError: If `use_locking` is not a bool.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``CPU``
 
     Examples:
         >>> input_x = Parameter(Tensor(np.array([[6.0, 6.0, 6.0], [2.0, 2.0, 2.0]]), mindspore.float32), name="x")

tests/st/ops/cpu/test_scatter_arithmetic_op.py

@@ -0,0 +1,643 @@
+# Copyright 2021 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 numpy as np
+import pytest
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor, Parameter
+from mindspore.ops import operations as P
+
+context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+
+
+class TestScatterAddNet(nn.Cell):
+    def __init__(self, lock, inputx, indices, updates):
+        super(TestScatterAddNet, self).__init__()
+        self.scatter_add = P.ScatterAdd(use_locking=lock)
+        self.inputx = Parameter(inputx, name="inputx")
+        self.indices = Parameter(indices, name="indices")
+        self.updates = Parameter(updates, name="updates")
+
+    def construct(self):
+        out = self.scatter_add(self.inputx, self.indices, self.updates)
+        return out
+
+
+def scatter_add_net(inputx, indices, updates):
+    lock = True
+    net = TestScatterAddNet(lock, inputx, indices, updates)
+    return net()
+
+
+def scatter_add_use_locking_false_net(inputx, indices, updates):
+    lock = False
+    net = TestScatterAddNet(lock, inputx, indices, updates)
+    return net()
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_add_small_float32():
+    inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
+    indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
+    updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
+    output = scatter_add_net(inputx, indices, updates)
+    expected = np.array([[6., 8., 10.],
+                         [12., 14., 16.]])
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_add_input_updated():
+    inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
+    indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
+    updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
+    lock = True
+    net = TestScatterAddNet(lock, inputx, indices, updates)
+    net()
+    expected = np.array([[6., 8., 10.],
+                         [12., 14., 16.]])
+    np.testing.assert_array_almost_equal(net.inputx.asnumpy(), expected)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_add_large_shape_float32():
+    inputx = Tensor(np.ones((4, 2, 3, 4)).astype(np.float32))
+    indices = Tensor(np.array([[0, 2], [3, 1]]).astype(np.int32))
+    updates = Tensor(np.arange(96).reshape((2, 2, 2, 3, 4)).astype(np.float32))
+    output = scatter_add_net(inputx, indices, updates)
+    expected = np.array([[[[1., 2., 3., 4.],
+                           [5., 6., 7., 8.],
+                           [9., 10., 11., 12.]],
+                          [[13., 14., 15., 16.],
+                           [17., 18., 19., 20.],
+                           [21., 22., 23., 24.]]],
+                         [[[73., 74., 75., 76.],
+                           [77., 78., 79., 80.],
+                           [81., 82., 83., 84.]],
+                          [[85., 86., 87., 88.],
+                           [89., 90., 91., 92.],
+                           [93., 94., 95., 96.]]],
+                         [[[25., 26., 27., 28.],
+                           [29., 30., 31., 32.],
+                           [33., 34., 35., 36.]],
+                          [[37., 38., 39., 40.],
+                           [41., 42., 43., 44.],
+                           [45., 46., 47., 48.]]],
+                         [[[49., 50., 51., 52.],
+                           [53., 54., 55., 56.],
+                           [57., 58., 59., 60.]],
+                          [[61., 62., 63., 64.],
+                           [65., 66., 67., 68.],
+                           [69., 70., 71., 72.]]]])
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_add_small_float32_use_locking_false():
+    inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
+    indices = Tensor(np.array([1, 0]).astype(np.int32))
+    updates = Tensor(np.arange(6).reshape((2, 3)).astype(np.float32))
+    output = scatter_add_use_locking_false_net(inputx, indices, updates)
+    expected = np.array([[3., 4., 5.],
+                         [0., 1., 2.]])
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_add_input_less_than_1_float32():
+    inputx = Tensor(np.array([[0.214141, 0.415151, 0.51516],
+                              [0.876542, 0.451611, 0.55112],
+                              [0.111244, 0.633333, 0.34444]]).astype(np.float32))
+    indices = Tensor(np.array([[[1, 0, 2],
+                                [2, 2, 0]],
+                               [[1, 0, 1],
+                                [2, 1, 2]]]).astype(np.int32))
+    updates = Tensor(np.arange(34, 70).reshape((2, 2, 3, 3)).astype(np.float32))
+    output = scatter_add_net(inputx, indices, updates)
+    expected = np.array([[141.21414, 144.41515, 147.51517],
+                         [208.87654, 212.45161, 216.55112],
+                         [257.11124, 262.63333, 267.34442]], dtype=np.float32)
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_add_float16():
+    inputx = Tensor(np.zeros((2, 3)).astype(np.float16))
+    indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
+    updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float16))
+    output = scatter_add_net(inputx, indices, updates)
+    expected = np.array([[6., 8., 10.],
+                         [12., 14., 16.]])
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_add_large_float16():
+    inputx = Tensor(np.zeros((2, 3, 4)).astype(np.float16))
+    indices = Tensor(np.array([[0, 0], [1, 1]]).astype(np.int32))
+    updates = Tensor(np.arange(63, 111).reshape((2, 2, 3, 4)).astype(np.float16))
+    output = scatter_add_net(inputx, indices, updates)
+    expected = np.array([[[138., 140., 142., 144.],
+                          [146., 148., 150., 152.],
+                          [154., 156., 158., 160.]],
+                         [[186., 188., 190., 192.],
+                          [194., 196., 198., 200.],
+                          [202., 204., 206., 208.]]])
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_add_disordered_float16():
+    inputx = Tensor(np.flip(np.arange(34, 46).reshape(3, 4).astype(np.float16)))
+    indices = Tensor(np.array([[[0, 1, 2],
+                                [2, 1, 0]],
+                               [[0, 0, 0],
+                                [2, 2, 2]]]).astype(np.int32))
+    updates = Tensor(np.arange(63, 111).reshape((2, 2, 3, 4)).astype(np.float16))
+    output = scatter_add_net(inputx, indices, updates)
+    expected = np.array([[464., 468., 472., 476.],
+                         [187., 188., 189., 190.],
+                         [492., 496., 500., 504.]])
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_add_large_int32():
+    inputx = Tensor(np.zeros((2, 3, 4)).astype(np.int32))
+    indices = Tensor(np.array([[0, 0], [1, 1]]).astype(np.int32))
+    updates = Tensor(np.arange(63, 111).reshape((2, 2, 3, 4)).astype(np.int32))
+    output = scatter_add_net(inputx, indices, updates)
+    expected = np.array([[[138., 140., 142., 144.],
+                          [146., 148., 150., 152.],
+                          [154., 156., 158., 160.]],
+                         [[186., 188., 190., 192.],
+                          [194., 196., 198., 200.],
+                          [202., 204., 206., 208.]]]).astype(np.int32)
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_add_disordered_int32():
+    inputx = Tensor(np.flip(np.arange(34, 46).reshape(3, 4).astype(np.int32)))
+    indices = Tensor(np.array([[[0, 1, 2],
+                                [2, 1, 0]],
+                               [[0, 0, 0],
+                                [2, 2, 2]]]).astype(np.int32))
+    updates = Tensor(np.arange(63, 111).reshape((2, 2, 3, 4)).astype(np.int32))
+    output = scatter_add_net(inputx, indices, updates)
+    expected = np.array([[464., 468., 472., 476.],
+                         [187., 188., 189., 190.],
+                         [492., 496., 500., 504.]]).astype(np.int32)
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
+
+class TestScatterSubNet(nn.Cell):
+    def __init__(self, lock, inputx, indices, updates):
+        super(TestScatterSubNet, self).__init__()
+        self.scatter_sub = P.ScatterSub(use_locking=lock)
+        self.inputx = Parameter(inputx, name="inputx")
+        self.indices = Parameter(indices, name="indices")
+        self.updates = Parameter(updates, name="updates")
+
+    def construct(self):
+        out = self.scatter_sub(self.inputx, self.indices, self.updates)
+        return out
+
+
+def scatter_sub_net(inputx, indices, updates):
+    lock = True
+    net = TestScatterSubNet(lock, inputx, indices, updates)
+    return net()
+
+
+def scatter_sub_use_locking_false_net(inputx, indices, updates):
+    lock = False
+    net = TestScatterSubNet(lock, inputx, indices, updates)
+    return net()
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_sub_input_updated():
+    inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
+    indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
+    updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
+    lock = True
+    net = TestScatterSubNet(lock, inputx, indices, updates)
+    net()
+    expected = np.array([[-6., -8., -10.],
+                         [-12., -14., -16.]])
+    np.testing.assert_array_almost_equal(net.inputx.asnumpy(), expected)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_sub_large_shape_float32():
+    inputx = Tensor(np.ones((4, 2, 3, 4)).astype(np.float32))
+    indices = Tensor(np.array([[0, 2], [3, 1]]).astype(np.int32))
+    updates = Tensor(np.arange(96).reshape((2, 2, 2, 3, 4)).astype(np.float32))
+    output = scatter_sub_net(inputx, indices, updates)
+    expected = np.array(
+        [[[[1.0, 0.0, -1.0, -2.0],
+           [-3.0, -4.0, -5.0, -6.0],
+           [-7.0, -8.0, -9.0, -10.0]],
+          [[-11.0, -12.0, -13.0, -14.0],
+           [-15.0, -16.0, -17.0, -18.0],
+           [-19.0, -20.0, -21.0, -22.0]]],
+         [[[-71.0, -72.0, -73.0, -74.0],
+           [-75.0, -76.0, -77.0, -78.0],
+           [-79.0, -80.0, -81.0, -82.0]],
+          [[-83.0, -84.0, -85.0, -86.0],
+           [-87.0, -88.0, -89.0, -90.0],
+           [-91.0, -92.0, -93.0, -94.0]]],
+         [[[-23.0, -24.0, -25.0, -26.0],
+           [-27.0, -28.0, -29.0, -30.0],
+           [-31.0, -32.0, -33.0, -34.0]],
+          [[-35.0, -36.0, -37.0, -38.0],
+           [-39.0, -40.0, -41.0, -42.0],
+           [-43.0, -44.0, -45.0, -46.0]]],
+         [[[-47.0, -48.0, -49.0, -50.0],
+           [-51.0, -52.0, -53.0, -54.0],
+           [-55.0, -56.0, -57.0, -58.0]],
+          [[-59.0, -60.0, -61.0, -62.0],
+           [-63.0, -64.0, -65.0, -66.0],
+           [-67.0, -68.0, -69.0, -70.0]]]])
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_sub_small_float32_use_locking_false():
+    inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
+    indices = Tensor(np.array([1, 0]).astype(np.int32))
+    updates = Tensor(np.arange(6).reshape((2, 3)).astype(np.float32))
+    output = scatter_sub_use_locking_false_net(inputx, indices, updates)
+    expected = np.array([[-3., -4., -5.],
+                         [-0., -1., -2.]])
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
+
+class TestScatterMulNet(nn.Cell):
+    def __init__(self, lock, inputx, indices, updates):
+        super(TestScatterMulNet, self).__init__()
+        self.scatter_mul = P.ScatterMul(use_locking=lock)
+        self.inputx = Parameter(inputx, name="inputx")
+        self.indices = Parameter(indices, name="indices")
+        self.updates = Parameter(updates, name="updates")
+
+    def construct(self):
+        out = self.scatter_mul(self.inputx, self.indices, self.updates)
+        return out
+
+
+def scatter_mul_net(inputx, indices, updates):
+    lock = True
+    net = TestScatterMulNet(lock, inputx, indices, updates)
+    return net()
+
+
+def scatter_mul_use_locking_false_net(inputx, indices, updates):
+    lock = False
+    net = TestScatterMulNet(lock, inputx, indices, updates)
+    return net()
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_mul_input_updated():
+    inputx = Tensor(np.ones((2, 3)).astype(np.float32))
+    indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
+    updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
+    lock = True
+    net = TestScatterMulNet(lock, inputx, indices, updates)
+    net()
+    expected = np.array([[0., 7., 16.],
+                         [27., 40., 55.]])
+    np.testing.assert_array_almost_equal(net.inputx.asnumpy(), expected)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_mul_output_updated_float32():
+    inputx = Tensor(np.ones((2, 3)).astype(np.float32))
+    indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
+    updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
+    output = scatter_mul_net(inputx, indices, updates)
+    expected = np.array([[0., 7., 16.],
+                         [27., 40., 55.]])
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_mul_small_float32_use_locking_false():
+    inputx = Tensor(np.ones((2, 3)).astype(np.float32))
+    indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
+    updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
+    output = scatter_mul_use_locking_false_net(inputx, indices, updates)
+    expected = np.array([[0., 7., 16.],
+                         [27., 40., 55.]])
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
+
+class TestScatterDivNet(nn.Cell):
+    def __init__(self, lock, inputx, indices, updates):
+        super(TestScatterDivNet, self).__init__()
+        self.scatter_div = P.ScatterDiv(use_locking=lock)
+        self.inputx = Parameter(inputx, name="inputx")
+        self.indices = Parameter(indices, name="indices")
+        self.updates = Parameter(updates, name="updates")
+
+    def construct(self):
+        out = self.scatter_div(self.inputx, self.indices, self.updates)
+        return out
+
+
+def scatter_div_net(inputx, indices, updates):
+    lock = True
+    net = TestScatterDivNet(lock, inputx, indices, updates)
+    return net()
+
+
+def scatter_div_use_locking_false_net(inputx, indices, updates):
+    lock = False
+    net = TestScatterDivNet(lock, inputx, indices, updates)
+    return net()
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_div_input_updated():
+    inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
+    indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
+    updates = Tensor(np.arange(1, 13).reshape((2, 2, 3)).astype(np.float32))
+    lock = True
+    net = TestScatterDivNet(lock, inputx, indices, updates)
+    net()
+    expected = np.array([[0., 0., 0.],
+                         [0., 0., 0.]])
+    np.testing.assert_array_almost_equal(net.inputx.asnumpy(), expected)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_div_output_updated_float32():
+    inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
+    indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
+    updates = Tensor(np.arange(1, 13).reshape((2, 2, 3)).astype(np.float32))
+    output = scatter_div_net(inputx, indices, updates)
+    expected = np.array([[0., 0., 0.],
+                         [0., 0., 0.]])
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_div_small_float32_use_locking_false():
+    inputx = Tensor(np.ones((2, 3)).astype(np.float32) * 10)
+    indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
+    updates = Tensor(np.ones(12).reshape((2, 2, 3)).astype(np.float32))
+    output = scatter_div_use_locking_false_net(inputx, indices, updates)
+    expected = np.array([[10., 10., 10.],
+                         [10., 10., 10.]])
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
+
+class TestScatterMaxNet(nn.Cell):
+    def __init__(self, lock, inputx, indices, updates):
+        super(TestScatterMaxNet, self).__init__()
+        self.scatter_max = P.ScatterMax(use_locking=lock)
+        self.inputx = Parameter(inputx, name="inputx")
+        self.indices = Parameter(indices, name="indices")
+        self.updates = Parameter(updates, name="updates")
+
+    def construct(self):
+        out = self.scatter_max(self.inputx, self.indices, self.updates)
+        return out
+
+
+def scatter_max_net(inputx, indices, updates):
+    lock = True
+    net = TestScatterMaxNet(lock, inputx, indices, updates)
+    return net()
+
+
+def scatter_max_use_locking_false_net(inputx, indices, updates):
+    lock = False
+    net = TestScatterMaxNet(lock, inputx, indices, updates)
+    return net()
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_max_input_updated():
+    inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
+    indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
+    updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
+    lock = True
+    net = TestScatterMaxNet(lock, inputx, indices, updates)
+    net()
+    expected = np.array([[6., 7., 8.],
+                         [9., 10., 11.]])
+    np.testing.assert_array_almost_equal(net.inputx.asnumpy(), expected)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_max_output_updated_float32():
+    inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
+    indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
+    updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
+    output = scatter_max_net(inputx, indices, updates)
+    expected = np.array([[6., 7., 8.],
+                         [9., 10., 11.]])
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_max_small_float32_use_locking_false():
+    inputx = Tensor(np.ones((2, 3)).astype(np.float32) * 10)
+    indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
+    updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
+    output = scatter_max_use_locking_false_net(inputx, indices, updates)
+    expected = np.array([[10., 10., 10.],
+                         [10., 10., 11.]])
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
+
+class TestScatterMinNet(nn.Cell):
+    def __init__(self, lock, inputx, indices, updates):
+        super(TestScatterMinNet, self).__init__()
+        self.scatter_min = P.ScatterMin(use_locking=lock)
+        self.inputx = Parameter(inputx, name="inputx")
+        self.indices = Parameter(indices, name="indices")
+        self.updates = Parameter(updates, name="updates")
+
+    def construct(self):
+        out = self.scatter_min(self.inputx, self.indices, self.updates)
+        return out
+
+
+def scatter_min_net(inputx, indices, updates):
+    lock = True
+    net = TestScatterMinNet(lock, inputx, indices, updates)
+    return net()
+
+
+def scatter_min_use_locking_false_net(inputx, indices, updates):
+    lock = False
+    net = TestScatterMinNet(lock, inputx, indices, updates)
+    return net()
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_min_input_updated():
+    inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
+    indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
+    updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
+    lock = True
+    net = TestScatterMinNet(lock, inputx, indices, updates)
+    net()
+    expected = np.array([[0., 0., 0.],
+                         [0., 0., 0.]])
+    np.testing.assert_array_almost_equal(net.inputx.asnumpy(), expected)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_min_output_updated_float32():
+    inputx = Tensor(np.ones((2, 3)).astype(np.float32))
+    indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
+    updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
+    output = scatter_min_net(inputx, indices, updates)
+    expected = np.array([[0., 1., 1.],
+                         [1., 1., 1.]])
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_min_small_float32_use_locking_false():
+    inputx = Tensor(np.ones((2, 3)).astype(np.float32))
+    indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
+    updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
+    output = scatter_min_use_locking_false_net(inputx, indices, updates)
+    expected = np.array([[0., 1., 1.],
+                         [1., 1., 1.]])
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
+
+class TestScatterUpdateNet(nn.Cell):
+    def __init__(self, lock, inputx, indices, updates):
+        super(TestScatterUpdateNet, self).__init__()
+        self.scatter_update = P.ScatterUpdate(use_locking=lock)
+        self.inputx = Parameter(inputx, name="inputx")
+        self.indices = Parameter(indices, name="indices")
+        self.updates = Parameter(updates, name="updates")
+
+    def construct(self):
+        out = self.scatter_update(self.inputx, self.indices, self.updates)
+        return out
+
+
+def scatter_update_net(inputx, indices, updates):
+    lock = True
+    net = TestScatterUpdateNet(lock, inputx, indices, updates)
+    return net()
+
+
+def scatter_update_use_locking_false_net(inputx, indices, updates):
+    lock = False
+    net = TestScatterUpdateNet(lock, inputx, indices, updates)
+    return net()
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_update_input_updated():
+    inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
+    indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
+    updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
+    lock = True
+    net = TestScatterUpdateNet(lock, inputx, indices, updates)
+    net()
+    expected = np.array([[6., 7., 8.],
+                         [9., 10., 11.]])
+    np.testing.assert_array_almost_equal(net.inputx.asnumpy(), expected)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_update_output_updated_float32():
+    inputx = Tensor(np.ones((2, 3)).astype(np.float32))
+    indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
+    updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
+    output = scatter_update_net(inputx, indices, updates)
+    expected = np.array([[6., 7., 8.],
+                         [9., 10., 11.]])
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_scatter_update_small_float32_use_locking_false():
+    inputx = Tensor(np.ones((2, 3)).astype(np.float32))
+    indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
+    updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
+    output = scatter_update_use_locking_false_net(inputx, indices, updates)
+    expected = np.array([[6., 7., 8.],
+                         [9., 10., 11.]])
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)