diff --git a/paddle/fluid/operators/elementwise/elementwise_add_op_mlu.cc b/paddle/fluid/operators/elementwise/elementwise_add_op_mlu.cc
new file mode 100644
index 0000000000000000000000000000000000000000..47a549dfcde28999fd86c1025bbcefb7a05282e7
--- /dev/null
+++ b/paddle/fluid/operators/elementwise/elementwise_add_op_mlu.cc
@@ -0,0 +1,154 @@
+/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+
+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 "paddle/fluid/operators/elementwise/elementwise_add_op.h"
+#include "paddle/fluid/operators/mlu/mlu_baseop.h"
+
+namespace paddle {
+namespace operators {
+using Tensor = framework::Tensor;
+
+template <typename T>
+class ElementwiseAddMLUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* x = ctx.Input<framework::LoDTensor>("X");
+    auto* y = ctx.Input<framework::LoDTensor>("Y");
+    auto* out = ctx.Output<framework::LoDTensor>("Out");
+    out->mutable_data<T>(ctx.GetPlace());
+
+    int axis = ctx.Attr<int>("axis");
+    const auto& x_dims = x->dims();
+    const auto& y_dims = y->dims();
+    axis = (axis < 0 ? (std::abs(x_dims.size() - y_dims.size()) + axis + 1)
+                     : axis);
+    int max_dim = std::max(x_dims.size(), y_dims.size());
+    std::vector<int> x_dims_array(max_dim);
+    std::vector<int> y_dims_array(max_dim);
+    std::vector<int> out_dims_array(max_dim);
+    GetBroadcastDimsArrays(x_dims, y_dims, x_dims_array.data(),
+                           y_dims_array.data(), out_dims_array.data(), max_dim,
+                           axis);
+
+    MLUCnnlTensorDesc x_desc(max_dim, x_dims_array.data(),
+                             ToCnnlDataType(x->type()));
+    MLUCnnlTensorDesc y_desc(max_dim, y_dims_array.data(),
+                             ToCnnlDataType(y->type()));
+    MLUCnnlTensorDesc out_desc(*out);
+    MLUCnnlOpTensorDesc op_tensor_desc(CNNL_OP_TENSOR_ADD, ToCnnlDataType<T>(),
+                                       CNNL_NOT_PROPAGATE_NAN);
+
+    MLUCnnl::OpTensor(ctx, op_tensor_desc.get(), x_desc.get(), GetBasePtr(x),
+                      y_desc.get(), GetBasePtr(y), out_desc.get(),
+                      GetBasePtr(out), ToCnnlDataType<T>());
+  }
+};
+
+template <typename T>
+class ElementwiseAddGradMLUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto& dev_ctx =
+        ctx.template device_context<paddle::platform::MLUDeviceContext>();
+    auto* x = ctx.Input<framework::Tensor>("X");
+    auto* y = ctx.Input<framework::Tensor>("Y");
+    auto* dout = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
+    auto* dx = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
+    auto* dy = ctx.Output<framework::Tensor>(framework::GradVarName("Y"));
+    int axis = ctx.Attr<int>("axis");
+    axis = (axis == -1 ? std::abs(x->dims().size() - y->dims().size()) : axis);
+
+    MLUCnnlTensorDesc dout_desc(*dout);
+    if (dx) {
+      dx->mutable_data<T>(ctx.GetPlace());
+      if (dx->dims() != dout->dims()) {
+        std::vector<int> dst_dims_vec;
+        std::vector<int> reduce_axes;
+        auto src_dims = dx->dims();
+        auto dout_dims = dout->dims();
+
+        int src_axis = (src_dims.size() < dout_dims.size() ? axis : 0);
+        for (int ax = 0; ax < dout_dims.size(); ++ax) {
+          if ((ax < src_axis || ax >= src_axis + src_dims.size()) ||
+              (dout_dims[ax] > 1 && src_dims[ax - src_axis] == 1)) {
+            reduce_axes.push_back(ax);
+          } else {
+            dst_dims_vec.push_back(dout_dims[ax]);
+          }
+        }
+        if (dst_dims_vec.size() == 0) {
+          // x is scalar
+          dst_dims_vec.push_back(1);
+        }
+
+        MLUCnnlReduceDesc reduction_desc(
+            reduce_axes, CNNL_REDUCE_ADD, ToCnnlDataType<T>(),
+            CNNL_NOT_PROPAGATE_NAN, CNNL_REDUCE_NO_INDICES, CNNL_32BIT_INDICES);
+        MLUCnnlTensorDesc dx_desc(dst_dims_vec.size(), dst_dims_vec.data(),
+                                  ToCnnlDataType<T>());
+        MLUCnnl::Reduce(ctx, true /*need_workspace*/, reduction_desc.get(),
+                        nullptr, dout_desc.get(), GetBasePtr(dout), 0, nullptr,
+                        nullptr, dx_desc.get(), GetBasePtr(dx));
+      } else {
+        framework::TensorCopy(*dout, ctx.GetPlace(), dev_ctx, dx);
+      }
+    }
+    if (dy) {
+      dy->mutable_data<T>(ctx.GetPlace());
+      if (dy->dims() != dout->dims()) {
+        std::vector<int> dst_dims_vec;
+        std::vector<int> reduce_axes;
+        auto src_dims = dy->dims();
+        auto dout_dims = dout->dims();
+
+        int src_axis = (src_dims.size() < dout_dims.size() ? axis : 0);
+        for (int ax = 0; ax < dout_dims.size(); ++ax) {
+          if ((ax < src_axis || ax >= src_axis + src_dims.size()) ||
+              (dout_dims[ax] > 1 && src_dims[ax - src_axis] == 1)) {
+            reduce_axes.push_back(ax);
+          } else {
+            dst_dims_vec.push_back(dout_dims[ax]);
+          }
+        }
+        if (dst_dims_vec.size() == 0) {
+          // y is scalar
+          dst_dims_vec.push_back(1);
+        }
+
+        MLUCnnlReduceDesc reduction_desc(
+            reduce_axes, CNNL_REDUCE_ADD, ToCnnlDataType<T>(),
+            CNNL_NOT_PROPAGATE_NAN, CNNL_REDUCE_NO_INDICES, CNNL_32BIT_INDICES);
+        MLUCnnlTensorDesc dy_desc(dst_dims_vec.size(), dst_dims_vec.data(),
+                                  ToCnnlDataType<T>());
+        MLUCnnl::Reduce(ctx, true /*need_workspace*/, reduction_desc.get(),
+                        nullptr, dout_desc.get(), GetBasePtr(dout), 0, nullptr,
+                        nullptr, dy_desc.get(), GetBasePtr(dy));
+      } else {
+        framework::TensorCopy(*dout, ctx.GetPlace(), dev_ctx, dy);
+      }
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+
+REGISTER_OP_MLU_KERNEL(elementwise_add, ops::ElementwiseAddMLUKernel<float>,
+                       ops::ElementwiseAddMLUKernel<plat::float16>);
+REGISTER_OP_MLU_KERNEL(elementwise_add_grad,
+                       ops::ElementwiseAddGradMLUKernel<float>,
+                       ops::ElementwiseAddGradMLUKernel<plat::float16>);
diff --git a/python/paddle/fluid/tests/unittests/mlu/test_elementwise_add_op_mlu.py b/python/paddle/fluid/tests/unittests/mlu/test_elementwise_add_op_mlu.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b6db6903fba0dc1d77b6026623a3bc3c101013c
--- /dev/null
+++ b/python/paddle/fluid/tests/unittests/mlu/test_elementwise_add_op_mlu.py
@@ -0,0 +1,527 @@
+#  Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+from __future__ import print_function
+import unittest
+import numpy as np
+import paddle
+import paddle.fluid.core as core
+import sys
+sys.path.append('..')
+from op_test import OpTest, skip_check_grad_ci
+import paddle.fluid as fluid
+from paddle.fluid import compiler, Program, program_guard
+
+
+class TestElementwiseAddOp(OpTest):
+    def set_mlu(self):
+        self.place = paddle.device.MLUPlace(0)
+        self.__class__.use_mlu = True
+
+    def setUp(self):
+        self.op_type = "elementwise_add"
+        self.set_mlu()
+        self.init_dtype()
+        self.init_input_output()
+        self.init_axis()
+
+        self.inputs = {
+            'X': OpTest.np_dtype_to_fluid_dtype(self.x),
+            'Y': OpTest.np_dtype_to_fluid_dtype(self.y)
+        }
+        self.attrs = {'axis': self.axis}
+        self.outputs = {'Out': self.out}
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+    def test_check_grad_normal(self):
+        if self.dtype == np.float16:
+            return
+        self.check_grad_with_place(
+            self.place, ['X', 'Y'], 'Out', max_relative_error=0.01)
+
+    def test_check_grad_ingore_x(self):
+        if self.dtype == np.float16:
+            return
+        self.check_grad_with_place(
+            self.place, ['Y'],
+            'Out',
+            no_grad_set=set("X"),
+            max_relative_error=0.01)
+
+    def test_check_grad_ingore_y(self):
+        if self.dtype == np.float16:
+            return
+        self.check_grad_with_place(
+            self.place, ['X'],
+            'Out',
+            no_grad_set=set('Y'),
+            max_relative_error=0.01)
+
+    def init_input_output(self):
+        self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
+        self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
+        self.out = np.add(self.x, self.y)
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+    def init_axis(self):
+        self.axis = -1
+
+
+class TestFP16ElementwiseAddOp(TestElementwiseAddOp):
+    def init_dtype(self):
+        self.dtype = np.float16
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, atol=1e-3)
+
+
+@skip_check_grad_ci(
+    reason="[skip shape check] Use y_shape(1) to test broadcast.")
+class TestElementwiseAddOp_scalar(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 4).astype(self.dtype)
+        self.y = np.random.rand(1).astype(self.dtype)
+        self.out = self.x + self.y
+
+
+@skip_check_grad_ci(
+    reason="[skip shape check] Use y_shape(1) to test broadcast.")
+class TestFP16ElementwiseAddOp_scalar(TestFP16ElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 4).astype(self.dtype)
+        self.y = np.random.rand(1).astype(self.dtype)
+        self.out = self.x + self.y
+
+
+@skip_check_grad_ci(
+    reason="[skip shape check] Use y_shape(1,1) to test broadcast.")
+class TestElementwiseAddOp_scalar2(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 4).astype(self.dtype)
+        self.y = np.random.rand(1, 1).astype(self.dtype)
+        self.out = self.x + self.y
+
+
+@skip_check_grad_ci(
+    reason="[skip shape check] Use y_shape(1,1) to test broadcast.")
+class TestFP16ElementwiseAddOp_scalar2(TestFP16ElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 4).astype(self.dtype)
+        self.y = np.random.rand(1, 1).astype(self.dtype)
+        self.out = self.x + self.y
+
+
+class TestElementwiseAddOp_Vector(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.random((100, )).astype(self.dtype)
+        self.y = np.random.random((100, )).astype(self.dtype)
+        self.out = np.add(self.x, self.y)
+
+
+class TestFP16ElementwiseAddOp_Vector(TestFP16ElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.random((100, )).astype(self.dtype)
+        self.y = np.random.random((100, )).astype(self.dtype)
+        self.out = np.add(self.x, self.y)
+
+
+class TestElementwiseAddOp_broadcast_0(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(100, 2, 3).astype(self.dtype)
+        self.y = np.random.rand(100).astype(self.dtype)
+        self.out = self.x + self.y.reshape(100, 1, 1)
+
+    def init_axis(self):
+        self.axis = 0
+
+
+class TestFP16ElementwiseAddOp_broadcast_0(TestFP16ElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(100, 2, 3).astype(self.dtype)
+        self.y = np.random.rand(100).astype(self.dtype)
+        self.out = self.x + self.y.reshape(100, 1, 1)
+
+    def init_axis(self):
+        self.axis = 0
+
+
+class TestElementwiseAddOp_broadcast_1(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 100, 3).astype(self.dtype)
+        self.y = np.random.rand(100).astype(self.dtype)
+        self.out = self.x + self.y.reshape(1, 100, 1)
+
+    def init_axis(self):
+        self.axis = 1
+
+
+class TestFP16ElementwiseAddOp_broadcast_1(TestFP16ElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 100, 3).astype(self.dtype)
+        self.y = np.random.rand(100).astype(self.dtype)
+        self.out = self.x + self.y.reshape(1, 100, 1)
+
+    def init_axis(self):
+        self.axis = 1
+
+
+class TestElementwiseAddOp_broadcast_2(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 100).astype(self.dtype)
+        self.y = np.random.rand(100).astype(self.dtype)
+        self.out = self.x + self.y.reshape(1, 1, 100)
+
+
+class TestFP16ElementwiseAddOp_broadcast_2(TestFP16ElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 100).astype(self.dtype)
+        self.y = np.random.rand(100).astype(self.dtype)
+        self.out = self.x + self.y.reshape(1, 1, 100)
+
+
+class TestElementwiseAddOp_broadcast_3(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 10, 12, 1).astype(self.dtype)
+        self.y = np.random.rand(10, 12).astype(self.dtype)
+        self.out = self.x + self.y.reshape(1, 10, 12, 1)
+
+    def init_axis(self):
+        self.axis = 1
+
+
+class TestFP16ElementwiseAddOp_broadcast_3(TestFP16ElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 10, 12, 3).astype(self.dtype)
+        self.y = np.random.rand(10, 12).astype(self.dtype)
+        self.out = self.x + self.y.reshape(1, 10, 12, 1)
+
+    def init_axis(self):
+        self.axis = 1
+
+
+class TestElementwiseAddOp_broadcast_4(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(100, 2, 1, 2).astype(self.dtype)
+        self.y = np.random.rand(100, 1).astype(self.dtype)
+        self.out = self.x + self.y.reshape(100, 1, 1, 1)
+
+    def init_axis(self):
+        self.axis = 0
+
+
+class TestFP16ElementwiseAddOp_broadcast_4(TestFP16ElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(100, 2, 1, 2).astype(self.dtype)
+        self.y = np.random.rand(100, 1).astype(self.dtype)
+        self.out = self.x + self.y.reshape(100, 1, 1, 1)
+
+    def init_axis(self):
+        self.axis = 0
+
+
+class TestElementwiseAddOp_broadcast_5(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(10, 3, 12).astype(self.dtype)
+        self.y = np.random.rand(10, 1, 12).astype(self.dtype)
+        self.out = self.x + self.y
+
+
+class TestFP16ElementwiseAddOp_broadcast_5(TestFP16ElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(10, 3, 12).astype(self.dtype)
+        self.y = np.random.rand(10, 1, 12).astype(self.dtype)
+        self.out = self.x + self.y
+
+
+class TestElementwiseAddOp_broadcast_6(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 12, 3, 5).astype(self.dtype)
+        self.y = np.random.rand(2, 12, 1, 5).astype(self.dtype)
+        self.out = self.x + self.y
+
+
+class TestElementwiseAddOp_broadcast_7(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(1, 1, 20, 5).astype(self.dtype)
+        self.y = np.random.rand(20, 5, 1, 1).astype(self.dtype)
+        self.out = self.x + self.y
+
+
+class TestFP16ElementwiseAddOp_broadcast_6(TestFP16ElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 12, 3, 5).astype(self.dtype)
+        self.y = np.random.rand(2, 12, 1, 5).astype(self.dtype)
+        self.out = self.x + self.y
+
+
+class TestElementwiseAddOp_rowwise_add_0(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 10, 12).astype(self.dtype)
+        self.y = np.random.rand(10, 12).astype(self.dtype)
+        self.out = self.x + self.y.reshape(1, 10, 12)
+
+    def init_axis(self):
+        self.axis = 1
+
+
+class TestFP16ElementwiseAddOp_rowwise_add_0(TestFP16ElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 10, 12).astype(self.dtype)
+        self.y = np.random.rand(10, 12).astype(self.dtype)
+        self.out = self.x + self.y.reshape(1, 10, 12)
+
+    def init_axis(self):
+        self.axis = 1
+
+
+@skip_check_grad_ci(
+    reason="[skip shape check] Use y_shape(1) to test broadcast.")
+class TestElementwiseAddOp_rowwise_add_1(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(100, 1).astype(self.dtype)
+        self.y = np.random.rand(1).astype(self.dtype)
+        self.out = self.x + self.y.reshape(1, 1)
+
+    def init_axis(self):
+        self.axis = 1
+
+
+@skip_check_grad_ci(
+    reason="[skip shape check] Use y_shape(1) to test broadcast.")
+class TestFP16ElementwiseAddOp_rowwise_add_1(TestFP16ElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(100, 1).astype(self.dtype)
+        self.y = np.random.rand(1).astype(self.dtype)
+        self.out = self.x + self.y.reshape(1, 1)
+
+    def init_axis(self):
+        self.axis = 1
+
+
+class TestElementwiseAddOp_channelwise_add(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(100, 2, 3).astype(self.dtype)
+        self.y = np.random.rand(100, 1, 1).astype(self.dtype)
+        self.out = self.x + self.y
+
+    def init_axis(self):
+        self.axis = -1
+
+
+class TestFP16ElementwiseAddOp_channelwise_add(TestFP16ElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(100, 2, 3).astype(self.dtype)
+        self.y = np.random.rand(100, 1, 1).astype(self.dtype)
+        self.out = self.x + self.y
+
+    def init_axis(self):
+        self.axis = -1
+
+
+class TestElementwiseAddOp_commonuse_add1(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 100).astype(self.dtype)
+        self.y = np.random.rand(1, 1, 100).astype(self.dtype)
+        self.out = self.x + self.y
+
+    def init_axis(self):
+        self.axis = -1
+
+
+class TestElementwiseFP16AddOp_commonuse_add1(TestFP16ElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 100).astype(self.dtype)
+        self.y = np.random.rand(1, 1, 100).astype(self.dtype)
+        self.out = self.x + self.y
+
+    def init_axis(self):
+        self.axis = -1
+
+
+class TestElementwiseAddOp_commonuse_add2(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(10, 3, 1, 4).astype(self.dtype)
+        self.y = np.random.rand(10, 1, 12, 1).astype(self.dtype)
+        self.out = self.x + self.y
+
+    def init_axis(self):
+        self.axis = -1
+
+
+class TestElementwiseAddOp_xsize_lessthan_ysize_add(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(10, 12).astype(self.dtype)
+        self.y = np.random.rand(2, 2, 10, 12).astype(self.dtype)
+        self.out = self.x + self.y
+
+    def init_axis(self):
+        self.axis = 2
+
+
+class TestElementwiseAddOp_same_shape_ysize_large(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(10, 1, 12).astype(self.dtype)
+        self.y = np.random.rand(10, 2, 12).astype(self.dtype)
+        self.out = self.x + self.y
+
+    def init_axis(self):
+        self.axis = 0
+
+
+class TestElementwiseAddOpError(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+            # the input of elementwise_add must be Variable.
+            x1 = fluid.create_lod_tensor(
+                np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.MLUPlace(0))
+            y1 = fluid.create_lod_tensor(
+                np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.MLUPlace(0))
+            self.assertRaises(TypeError, fluid.layers.elementwise_add, x1, y1)
+
+            # the input dtype of elementwise_add must be float16 or float32
+            x2 = fluid.layers.data(name='x2', shape=[3, 4, 5, 6], dtype="uint8")
+            y2 = fluid.layers.data(name='y2', shape=[3, 4, 5, 6], dtype="uint8")
+            self.assertRaises(TypeError, fluid.layers.elementwise_add, x2, y2)
+
+
+class TestAddApi(unittest.TestCase):
+    def _executed_api(self, x, y, name=None):
+        return paddle.add(x, y, name)
+
+    def test_name(self):
+        with fluid.program_guard(fluid.Program()):
+            x = fluid.data(name="x", shape=[2, 3], dtype="float32")
+            y = fluid.data(name='y', shape=[2, 3], dtype='float32')
+
+            y_1 = self._executed_api(x, y, name='add_res')
+            self.assertEqual(('add_res' in y_1.name), True)
+
+    def test_declarative(self):
+        with fluid.program_guard(fluid.Program()):
+
+            def gen_data():
+                return {
+                    "x": np.array([2, 3, 4]).astype('float32'),
+                    "y": np.array([1, 5, 2]).astype('float32')
+                }
+
+            x = fluid.data(name="x", shape=[3], dtype='float32')
+            y = fluid.data(name="y", shape=[3], dtype='float32')
+            z = self._executed_api(x, y)
+
+            place = fluid.MLUPlace(0)
+            exe = fluid.Executor(place)
+            z_value = exe.run(feed=gen_data(), fetch_list=[z.name])
+            z_expected = np.array([3., 8., 6.])
+            self.assertEqual((z_value == z_expected).all(), True)
+
+    def test_dygraph(self):
+        with fluid.dygraph.guard():
+            np_x = np.array([2, 3, 4]).astype('float32')
+            np_y = np.array([1, 5, 2]).astype('float32')
+            x = fluid.dygraph.to_variable(np_x)
+            y = fluid.dygraph.to_variable(np_y)
+            z = self._executed_api(x, y)
+            np_z = z.numpy()
+            z_expected = np.array([3., 8., 6.])
+            self.assertEqual((np_z == z_expected).all(), True)
+
+
+class TestAddInplaceApi(TestAddApi):
+    def _executed_api(self, x, y, name=None):
+        return x.add_(y, name)
+
+
+class TestAddInplaceBroadcastSuccess(unittest.TestCase):
+    def init_data(self):
+        self.x_numpy = np.random.rand(2, 3, 4).astype('float32')
+        self.y_numpy = np.random.rand(3, 4).astype('float32')
+
+    def test_broadcast_success(self):
+        paddle.disable_static()
+        self.init_data()
+        x = paddle.to_tensor(self.x_numpy)
+        y = paddle.to_tensor(self.y_numpy)
+        inplace_result = x.add_(y)
+        numpy_result = self.x_numpy + self.y_numpy
+        self.assertEqual((inplace_result.numpy() == numpy_result).all(), True)
+        paddle.enable_static()
+
+
+class TestAddInplaceBroadcastSuccess2(TestAddInplaceBroadcastSuccess):
+    def init_data(self):
+        self.x_numpy = np.random.rand(1, 2, 3, 1).astype('float32')
+        self.y_numpy = np.random.rand(3, 1).astype('float32')
+
+
+class TestAddInplaceBroadcastSuccess3(TestAddInplaceBroadcastSuccess):
+    def init_data(self):
+        self.x_numpy = np.random.rand(2, 3, 1, 5).astype('float32')
+        self.y_numpy = np.random.rand(1, 3, 1, 5).astype('float32')
+
+
+class TestAddInplaceBroadcastError(unittest.TestCase):
+    def init_data(self):
+        self.x_numpy = np.random.rand(3, 4).astype('float32')
+        self.y_numpy = np.random.rand(2, 3, 4).astype('float32')
+
+    def test_broadcast_errors(self):
+        paddle.disable_static()
+        self.init_data()
+        x = paddle.to_tensor(self.x_numpy)
+        y = paddle.to_tensor(self.y_numpy)
+
+        def broadcast_shape_error():
+            x.add_(y)
+
+        self.assertRaises(ValueError, broadcast_shape_error)
+        paddle.enable_static()
+
+
+class TestAddInplaceBroadcastError2(TestAddInplaceBroadcastError):
+    def init_data(self):
+        self.x_numpy = np.random.rand(2, 1, 4).astype('float32')
+        self.y_numpy = np.random.rand(2, 3, 4).astype('float32')
+
+
+class TestAddInplaceBroadcastError3(TestAddInplaceBroadcastError):
+    def init_data(self):
+        self.x_numpy = np.random.rand(5, 2, 1, 4).astype('float32')
+        self.y_numpy = np.random.rand(2, 3, 4).astype('float32')
+
+
+class TestBoolAddFloatElementwiseAddop(unittest.TestCase):
+    def test_static_add(self):
+        paddle.enable_static()
+        a = 1.5
+        b = paddle.full([4, 5, 6], True, dtype='bool')
+        c = a + b
+        self.assertTrue(c.dtype == core.VarDesc.VarType.FP32)
+        paddle.enable_static()
+
+    def test_dygraph_add(self):
+        paddle.disable_static()
+        a = 1.5
+        b = paddle.full([4, 5, 6], True, dtype='bool')
+        c = a + b
+        self.assertTrue(c.dtype == core.VarDesc.VarType.FP32)
+
+
+if __name__ == '__main__':
+    paddle.enable_static()
+    unittest.main()