diff --git a/mindspore/ccsrc/backend/kernel_compiler/cpu/maximum_cpu_kernel.cc b/mindspore/ccsrc/backend/kernel_compiler/cpu/maximum_cpu_kernel.cc
new file mode 100644
index 0000000000..68af12668f
--- /dev/null
+++ b/mindspore/ccsrc/backend/kernel_compiler/cpu/maximum_cpu_kernel.cc
@@ -0,0 +1,210 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "backend/kernel_compiler/cpu/maximum_cpu_kernel.h"
+#include "runtime/device/cpu/cpu_device_address.h"
+
+namespace mindspore {
+namespace kernel {
+
+template <typename T>
+void MaximumCPUKernel<T>::InitKernel(const CNodePtr &kernel_node) {
+  MS_EXCEPTION_IF_NULL(kernel_node);
+  input_x_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
+  input_y_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 1);
+  output_shape_ = AnfAlgo::GetOutputDeviceShape(kernel_node, 0);
+  TypeId input_x_dtype = AnfAlgo::GetInputDeviceDataType(kernel_node, 0);
+  TypeId input_y_dtype = AnfAlgo::GetInputDeviceDataType(kernel_node, 1);
+  size_t max_input_shape_size =
+    input_x_shape_.size() > input_y_shape_.size() ? input_x_shape_.size() : input_y_shape_.size();
+  for (size_t i = 0; i < output_shape_.size(); i++) {
+    output_num_ *= output_shape_[i];
+  }
+  if ((input_x_shape_.size() == 0 && input_y_shape_.size() != 0) ||
+      (input_x_shape_.size() != 0 && input_y_shape_.size() == 0)) {
+    InitInputTensorAndScalar(max_input_shape_size);
+  } else if (max_input_shape_size == output_shape_.size() && output_shape_.size() != 0) {
+    InitInputTensors(input_x_dtype, input_y_dtype);
+  } else {
+    MS_LOG(EXCEPTION) << "Only support input two tensors or one tensor and one scalar";
+  }
+}
+
+template <typename T>
+void MaximumCPUKernel<T>::InitInputTensorAndScalar(size_t max_input_shape_size) {
+  if (max_input_shape_size != output_shape_.size()) {
+    MS_LOG(EXCEPTION) << "Output tensor size must be equal to the max shape size of inputs";
+  }
+  need_broadcast_ = false;
+}
+
+template <typename T>
+void MaximumCPUKernel<T>::InitInputTensors(TypeId input_x_dtype, TypeId input_y_dtype) {
+  if (input_x_dtype == kNumberTypeBool && input_y_dtype == kNumberTypeBool) {
+    MS_LOG(EXCEPTION) << "Input tensor types cannot be both bool";
+  }
+  // Check if the shape needs to be broadcast
+  need_broadcast_ = IsBroadcast();
+  if (need_broadcast_) {
+    InitTensorBroadcastShape();
+  }
+}
+
+template <typename T>
+bool MaximumCPUKernel<T>::Launch(const std::vector<kernel::AddressPtr> &inputs,
+                                 const std::vector<kernel::AddressPtr> & /*workspace*/,
+                                 const std::vector<kernel::AddressPtr> &outputs) {
+  T *input_x_ = reinterpret_cast<T *>(inputs[0]->addr);
+  T *input_y_ = reinterpret_cast<T *>(inputs[1]->addr);
+  T *output_ = reinterpret_cast<T *>(outputs[0]->addr);
+
+  BroadcastArith(input_x_, input_y_, output_);
+  return true;
+}
+
+template <typename T>
+void MaximumCPUKernel<T>::BroadcastArith(const T *input_x, const T *input_y, T *output) {
+  MS_EXCEPTION_IF_NULL(input_x);
+  MS_EXCEPTION_IF_NULL(input_y);
+  MS_EXCEPTION_IF_NULL(output);
+  if (need_broadcast_) {
+    BroadcastArithKernel(broadcast_input_x_shape_[0], broadcast_input_x_shape_[1], broadcast_input_x_shape_[2],
+                         broadcast_input_x_shape_[3], broadcast_input_x_shape_[4], broadcast_input_x_shape_[5],
+                         broadcast_input_x_shape_[6], broadcast_input_y_shape_[0], broadcast_input_y_shape_[1],
+                         broadcast_input_y_shape_[2], broadcast_input_y_shape_[3], broadcast_input_y_shape_[4],
+                         broadcast_input_y_shape_[5], broadcast_input_y_shape_[6], broadcast_output_shape_[0],
+                         broadcast_output_shape_[1], broadcast_output_shape_[2], broadcast_output_shape_[3],
+                         broadcast_output_shape_[4], broadcast_output_shape_[5], broadcast_output_shape_[6], input_x,
+                         input_y, output);
+  } else {
+    if (input_x_shape_.size() == 0 || input_y_shape_.size() == 0) {
+      BroadcastArithOneScalarOneTensor(input_x, input_y, output);
+    } else {
+      BroadcastArithTensors(input_x, input_y, output);
+    }
+  }
+}
+
+template <typename T>
+bool MaximumCPUKernel<T>::IsBroadcast() {
+  if (input_x_shape_.size() != input_y_shape_.size()) {
+    return true;
+  }
+  for (size_t i = 0; i < input_x_shape_.size(); i++) {
+    if (input_x_shape_[i] != input_y_shape_[i]) {
+      return true;
+    }
+  }
+  return false;
+}
+
+template <typename T>
+void MaximumCPUKernel<T>::InitTensorBroadcastShape() {
+  if (output_shape_.size() > max_dims) {
+    MS_LOG(EXCEPTION) << "Broadcast operation not support dim greater than 7";
+  }
+  broadcast_input_x_shape_.resize(max_dims, 1);
+  broadcast_input_y_shape_.resize(max_dims, 1);
+  broadcast_output_shape_.resize(max_dims, 1);
+  for (size_t i = 0; i < output_shape_.size(); i++) {
+    broadcast_output_shape_[i] = output_shape_[i];
+  }
+  int input_x_dim_offset = output_shape_.size() - input_x_shape_.size();
+  for (size_t j = 0; j < input_x_shape_.size(); j++) {
+    broadcast_input_x_shape_[j + input_x_dim_offset] = input_x_shape_[j];
+    input_x_num_ *= input_x_shape_[j];
+  }
+  int input_y_dim_offset = output_shape_.size() - input_y_shape_.size();
+  for (size_t k = 0; k < input_y_shape_.size(); k++) {
+    if (need_broadcast_) {
+      broadcast_input_y_shape_[k + input_y_dim_offset] = input_y_shape_[k];
+      input_y_num_ *= input_y_shape_[k];
+    }
+  }
+}
+
+// Broadcast comparation
+template <typename T>
+size_t MaximumCPUKernel<T>::Index(const size_t &index, const size_t &dim) {
+  return dim == 1 ? 0 : index;
+}
+
+// Broadcast Arithmetic
+template <typename T>
+void MaximumCPUKernel<T>::BroadcastArithKernel(const size_t l0, const size_t l1, const size_t l2, const size_t l3,
+                                               const size_t l4, const size_t l5, const size_t l6, const size_t r0,
+                                               const size_t r1, const size_t r2, const size_t r3, const size_t r4,
+                                               const size_t r5, const size_t r6, const size_t d0, const size_t d1,
+                                               const size_t d2, const size_t d3, const size_t d4, const size_t d5,
+                                               const size_t d6, const T *input_x, const T *input_y, T *output) {
+  MS_EXCEPTION_IF_NULL(input_x);
+  MS_EXCEPTION_IF_NULL(input_y);
+  MS_EXCEPTION_IF_NULL(output);
+  for (size_t pos = 0; pos < output_num_; pos++) {
+    size_t i = pos / (d1 * d2 * d3 * d4 * d5 * d6) % d0;
+    size_t j = pos / (d2 * d3 * d4 * d5 * d6) % d1;
+    size_t k = pos / (d3 * d4 * d5 * d6) % d2;
+    size_t l = pos / (d4 * d5 * d6) % d3;
+    size_t m = pos / (d5 * d6) % d4;
+    size_t n = pos / d6 % d5;
+    size_t o = pos % d6;
+
+    size_t l_index = Index(i, l0) * l1 * l2 * l3 * l4 * l5 * l6;
+    l_index += Index(j, l1) * l2 * l3 * l4 * l5 * l6;
+    l_index += Index(k, l2) * l3 * l4 * l5 * l6;
+    l_index += Index(l, l3) * l4 * l5 * l6;
+    l_index += Index(m, l4) * l5 * l6;
+    l_index += Index(n, l5) * l6;
+    l_index += Index(o, l6);
+    size_t r_index = Index(i, r0) * r1 * r2 * r3 * r4 * r5 * r6;
+    r_index += Index(j, r1) * r2 * r3 * r4 * r5 * r6;
+    r_index += Index(k, r2) * r3 * r4 * r5 * r6;
+    r_index += Index(l, r3) * r4 * r5 * r6;
+    r_index += Index(m, r4) * r5 * r6;
+    r_index += Index(n, r5) * r6;
+    r_index += Index(o, r6);
+    output[pos] = MaximumFunc(input_x[l_index], input_y[r_index]);
+  }
+}
+
+template <typename T>
+void MaximumCPUKernel<T>::BroadcastArithOneScalarOneTensor(const T *input_x, const T *input_y, T *output) {
+  MS_EXCEPTION_IF_NULL(input_x);
+  MS_EXCEPTION_IF_NULL(input_y);
+  MS_EXCEPTION_IF_NULL(output);
+  if (input_x_shape_.size() == 0) {
+    for (size_t i = 0; i < output_num_; ++i) {
+      output[i] = MaximumFunc(input_x[0], input_y[i]);
+    }
+  } else {
+    for (size_t i = 0; i < output_num_; ++i) {
+      output[i] = MaximumFunc(input_x[i], input_y[0]);
+    }
+  }
+}
+
+template <typename T>
+void MaximumCPUKernel<T>::BroadcastArithTensors(const T *input_x, const T *input_y, T *output) {
+  MS_EXCEPTION_IF_NULL(input_x);
+  MS_EXCEPTION_IF_NULL(input_y);
+  MS_EXCEPTION_IF_NULL(output);
+  for (size_t i = 0; i < output_num_; ++i) {
+    output[i] = MaximumFunc(input_x[i], input_y[i]);
+  }
+}
+
+}  // namespace kernel
+}  // namespace mindspore
diff --git a/mindspore/ccsrc/backend/kernel_compiler/cpu/maximum_cpu_kernel.h b/mindspore/ccsrc/backend/kernel_compiler/cpu/maximum_cpu_kernel.h
new file mode 100644
index 0000000000..4342821fbb
--- /dev/null
+++ b/mindspore/ccsrc/backend/kernel_compiler/cpu/maximum_cpu_kernel.h
@@ -0,0 +1,122 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_MAXIMUM_CPU_KERNEL_H_
+#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_MAXIMUM_CPU_KERNEL_H_
+
+#include <vector>
+#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 MaximumCPUKernel : public CPUKernel {
+ public:
+  MaximumCPUKernel() = default;
+  ~MaximumCPUKernel() 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:
+  bool IsBroadcast();
+
+  size_t Index(const size_t &index, const size_t &dim);
+
+  void InitTensorBroadcastShape();
+
+  void InitInputTensorAndScalar(size_t max_input_shape_size);
+
+  void InitInputTensors(TypeId input_x_dtype, TypeId input_y_dtype);
+
+  // Broadcast Arithmetic
+  void BroadcastArithKernel(const size_t l0, const size_t l1, const size_t l2, const size_t l3, const size_t l4,
+                            const size_t l5, const size_t l6, const size_t r0, const size_t r1, const size_t r2,
+                            const size_t r3, const size_t r4, const size_t r5, const size_t r6, const size_t d0,
+                            const size_t d1, const size_t d2, const size_t d3, const size_t d4, const size_t d5,
+                            const size_t d6, const T *input_x, const T *input_y, T *output);
+
+  T MaximumFunc(const T &lhs, const T &rhs) { return lhs > rhs ? lhs : rhs; }
+
+  void BroadcastArithOneScalarOneTensor(const T *input_x, const T *input_y, T *output);
+
+  void BroadcastArithTensors(const T *input_x, const T *input_y, T *output);
+
+  void BroadcastArith(const T *input_x, const T *input_y, T *output);
+
+ private:
+  bool need_broadcast_{false};
+  size_t input_x_num_{1};
+  size_t input_y_num_{1};
+  size_t output_num_{1};
+  std::vector<size_t> input_x_shape_;
+  std::vector<size_t> input_y_shape_;
+  std::vector<size_t> output_shape_;
+  std::vector<size_t> broadcast_input_x_shape_;
+  std::vector<size_t> broadcast_input_y_shape_;
+  std::vector<size_t> broadcast_output_shape_;
+  const size_t max_dims{7};
+};
+
+MS_REG_CPU_KERNEL_T(
+  Maximum, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
+  MaximumCPUKernel, int32_t);
+
+MS_REG_CPU_KERNEL_T(
+  Maximum,
+  KernelAttr().AddInputAttr(kNumberTypeUInt32).AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt32),
+  MaximumCPUKernel, uint32_t);
+
+MS_REG_CPU_KERNEL_T(
+  Maximum,
+  KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+  MaximumCPUKernel, float);
+
+MS_REG_CPU_KERNEL_T(
+  Maximum, KernelAttr().AddInputAttr(kNumberTypeBool).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
+  MaximumCPUKernel, int32_t);
+
+MS_REG_CPU_KERNEL_T(
+  Maximum, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeInt32),
+  MaximumCPUKernel, int32_t);
+
+MS_REG_CPU_KERNEL_T(
+  Maximum, KernelAttr().AddInputAttr(kNumberTypeBool).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
+  MaximumCPUKernel, int64_t);
+
+MS_REG_CPU_KERNEL_T(
+  Maximum, KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeInt64),
+  MaximumCPUKernel, int64_t);
+
+MS_REG_CPU_KERNEL_T(
+  Maximum, KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
+  MaximumCPUKernel, int64_t);
+
+MS_REG_CPU_KERNEL_T(
+  Maximum,
+  KernelAttr().AddInputAttr(kNumberTypeUInt64).AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeUInt64),
+  MaximumCPUKernel, uint64_t);
+
+MS_REG_CPU_KERNEL_T(
+  Maximum,
+  KernelAttr().AddInputAttr(kNumberTypeFloat64).AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
+  MaximumCPUKernel, double);
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_UPDATE_CACHE_CPU_KERNEL_H_
diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py
index ea0f8448d1..986bcd7c7b 100644
--- a/mindspore/ops/operations/math_ops.py
+++ b/mindspore/ops/operations/math_ops.py
@@ -1876,7 +1876,7 @@ class Maximum(_MathBinaryOp):
         and the data type is the one with higher precision or higher digits among the two inputs.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> input_x = Tensor(np.array([1.0, 5.0, 3.0]), mindspore.float32)
diff --git a/tests/st/ops/cpu/test_maximum_op.py b/tests/st/ops/cpu/test_maximum_op.py
new file mode 100644
index 0000000000..3a34af59bc
--- /dev/null
+++ b/tests/st/ops/cpu/test_maximum_op.py
@@ -0,0 +1,193 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import numpy as np
+import pytest
+
+import mindspore.context as context
+from mindspore.common.tensor import Tensor
+from mindspore.nn import Cell
+from mindspore.ops import operations as P
+
+
+class ConstScalarAndTensorMaximum(Cell):
+    def __init__(self):
+        super(ConstScalarAndTensorMaximum, self).__init__()
+        self.max = P.Maximum()
+        self.x = 20
+
+    def construct(self, y):
+        return self.max(self.x, y)
+
+
+class TwoTensorsMaximum(Cell):
+    def __init__(self):
+        super(TwoTensorsMaximum, self).__init__()
+        self.max = P.Maximum()
+
+    def construct(self, x, y):
+        return self.max(x, y)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu_training
+@pytest.mark.env_onecard
+def test_maximum_constScalar_tensor_int():
+    x = Tensor(np.array([[2, 3, 4], [100, 200, 300]]).astype(np.int32))
+    expect = [[20, 20, 20], [100, 200, 300]]
+    error = np.ones(shape=[2, 3]) * 1.0e-5
+
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    max_op = ConstScalarAndTensorMaximum()
+    output = max_op(x)
+    diff = output.asnumpy() - expect
+    assert np.all(diff < error)
+    assert np.all(-diff < error)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu_training
+@pytest.mark.env_onecard
+def test_maximum_two_tensors_Not_Broadcast_int():
+    x = Tensor(np.array([[2, 3, 4], [100, 200, 300]]).astype(np.int32))
+    y = Tensor(np.array([[1, 2, 3], [100, 100, 200]]).astype(np.int32))
+    expect = [[2, 3, 4], [100, 200, 300]]
+    error = np.ones(shape=[2, 3]) * 1.0e-5
+
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    max_op = TwoTensorsMaximum()
+    output = max_op(x, y)
+    diff = output.asnumpy() - expect
+    assert np.all(diff < error)
+    assert np.all(-diff < error)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu_training
+@pytest.mark.env_onecard
+def test_maximum_two_tensors_Broadcast_int():
+    x = Tensor(np.array([[2, 3, 4], [100, 200, 300]]).astype(np.int32))
+    y = Tensor(np.array([[100, 100, 200]]).astype(np.int32))
+    expect = [[100, 100, 200], [100, 200, 300]]
+    error = np.ones(shape=[2, 3]) * 1.0e-5
+
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    max_op = TwoTensorsMaximum()
+    output = max_op(x, y)
+    diff = output.asnumpy() - expect
+    assert np.all(diff < error)
+    assert np.all(-diff < error)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu_training
+@pytest.mark.env_onecard
+def test_maximum_two_tensors_Broadcast_oneDimension_int():
+    x = Tensor(np.array([[2, 3, 4], [100, 200, 300]]).astype(np.int32))
+    y = Tensor(np.array([[100]]).astype(np.int32))
+    expect = [[100, 100, 100], [100, 200, 300]]
+    error = np.ones(shape=[2, 3]) * 1.0e-5
+
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    max_op = TwoTensorsMaximum()
+    output = max_op(x, y)
+    diff = output.asnumpy() - expect
+    assert np.all(diff < error)
+    assert np.all(-diff < error)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu_training
+@pytest.mark.env_onecard
+def test_maximum_two_tensors_notBroadcast_all_oneDimension_int():
+    x = Tensor(np.array([[2]]).astype(np.int32))
+    y = Tensor(np.array([[100]]).astype(np.int32))
+    expect = [[100]]
+    error = np.ones(shape=[1, 1]) * 1.0e-5
+
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    max_op = TwoTensorsMaximum()
+    output = max_op(x, y)
+    diff = output.asnumpy() - expect
+    assert np.all(diff < error)
+    assert np.all(-diff < error)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu_training
+@pytest.mark.env_onecard
+def test_maximum_two_tensors_Broadcast_bool():
+    x = Tensor(np.array([[2, 2]]).astype(np.int32))
+    y = Tensor(np.array([[True, False], [False, False]]).astype(np.bool_))
+    expect = [[2, 2], [2, 2]]
+    error = np.ones(shape=[2, 2]) * 1.0e-5
+
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    max_op = TwoTensorsMaximum()
+    output = max_op(x, y)
+    diff = output.asnumpy() - expect
+    assert np.all(diff < error)
+    assert np.all(-diff < error)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu_training
+@pytest.mark.env_onecard
+def test_maximum_two_tensors_notBroadcast_bool():
+    x = Tensor(np.array([[2, 2], [-1, 100]]).astype(np.int32))
+    y = Tensor(np.array([[True, False], [False, False]]).astype(np.bool_))
+    expect = [[2, 2], [0, 100]]
+    error = np.ones(shape=[2, 2]) * 1.0e-5
+
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    max_op = TwoTensorsMaximum()
+    output = max_op(x, y)
+    diff = output.asnumpy() - expect
+    assert np.all(diff < error)
+    assert np.all(-diff < error)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu_training
+@pytest.mark.env_onecard
+def test_maximum_two_tensors_notBroadcast_float32():
+    x = Tensor(np.array([[2.0, 2.0], [-1, 100]]).astype(np.float32))
+    y = Tensor(np.array([[1.0, 2.1], [-0.8, 100.5]]).astype(np.float32))
+    expect = [[2.0, 2.1], [-0.8, 100.5]]
+    error = np.ones(shape=[2, 2]) * 1.0e-5
+
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    max_op = TwoTensorsMaximum()
+    output = max_op(x, y)
+    diff = output.asnumpy() - expect
+    assert np.all(diff < error)
+    assert np.all(-diff < error)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu_training
+@pytest.mark.env_onecard
+def test_maximum_two_tensors_notBroadcast_float64():
+    x = Tensor(np.array([[2.0, 2.0], [-1, 100]]).astype(np.float64))
+    y = Tensor(np.array([[1.0, 2.1], [-0.8, 100.5]]).astype(np.float64))
+    expect = [[2.0, 2.1], [-0.8, 100.5]]
+    error = np.ones(shape=[2, 2]) * 1.0e-5
+
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    max_op = TwoTensorsMaximum()
+    output = max_op(x, y)
+    diff = output.asnumpy() - expect
+    assert np.all(diff < error)
+    assert np.all(-diff < error)