diff --git a/paddle/fluid/operators/deformable_conv_op_xpu.cc b/paddle/fluid/operators/deformable_conv_op_xpu.cc
new file mode 100644
index 0000000000000000000000000000000000000000..8dc5e59ee95716b90dc82264383a5e557c7bbe39
--- /dev/null
+++ b/paddle/fluid/operators/deformable_conv_op_xpu.cc
@@ -0,0 +1,288 @@
+/* Copyright (c) 2020 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. */
+
+#ifdef PADDLE_WITH_XPU
+#include <algorithm>
+#include <vector>
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/platform/xpu_header.h"
+#include "xpu/refactor/math.h"
+#include "xpu/refactor/nn.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename DeviceContext, typename T>
+class DeformableConvXPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* input = ctx.Input<Tensor>("Input");
+    auto* offset = ctx.Input<Tensor>("Offset");
+    auto* mask = ctx.Input<Tensor>("Mask");
+    Tensor filter = *ctx.Input<Tensor>("Filter");
+    Tensor* output = ctx.Output<Tensor>("Output");
+    output->mutable_data<T>(ctx.GetPlace());
+
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+
+    const int groups = ctx.Attr<int>("groups");
+    const int deformable_groups = ctx.Attr<int>("deformable_groups");
+    const int im2col_step = ctx.Attr<int>("im2col_step");
+    const std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
+    const std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
+    const std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
+
+    PADDLE_ENFORCE_EQ(
+        deformable_groups == 1, true,
+        platform::errors::InvalidArgument((
+            "XPU only support deformable_groups == 1 in deformable_conv op.")));
+    PADDLE_ENFORCE_EQ(
+        groups == 1, true,
+        platform::errors::InvalidArgument(
+            ("XPU only support groups == 1 in deformable_conv op.")));
+    PADDLE_ENFORCE_EQ(filter.dims()[2] <= 8 && filter.dims()[3] <= 8, true,
+                      platform::errors::InvalidArgument(
+                          "Filter high and weight should less than 8 on xpu "
+                          "in deformable_conv op."));
+
+    const int batch_size = static_cast<int>(input->dims()[0]);
+    std::vector<int64_t> output_shape_vec(framework::vectorize(output->dims()));
+
+    const T* input_ptr = input->data<T>();
+    const T* filter_ptr = filter.data<T>();
+    const float* offset_ptr = offset->data<T>();
+    const float* mask_ptr = mask->data<T>();
+    T* output_prt = output->data<T>();
+
+    // set zeros for d_table_data
+    const int zero = 0;
+    int r = xpu::constant<T>(dev_ctx.x_context(), output_prt, output->numel(),
+                             zero);
+    PADDLE_ENFORCE_EQ(r == xpu::Error_t::SUCCESS, true,
+                      platform::errors::External(
+                          "XPU API return wrong value[%d], please check where "
+                          "Baidu Kunlun Card is properly installed.",
+                          r));
+    int input_dim = input->numel() / input->dims()[0];
+    int input_offset_dim = offset->numel() / offset->dims()[0];
+    int input_mask_dim = mask->numel() / mask->dims()[0];
+    int output_dim =
+        output_shape_vec[1] * output_shape_vec[2] * output_shape_vec[3];
+    std::vector<int> ksize{static_cast<int>(filter.dims()[2]),
+                           static_cast<int>(filter.dims()[3])};
+    int n = im2col_step;
+    int c = input->dims()[1];
+    int h = input->dims()[2];
+    int w = input->dims()[3];
+    int f = filter.dims()[0];
+
+    for (int i = 0; i < batch_size / im2col_step; ++i) {
+      int r = xpu::deformable_conv<float, float, float, int>(
+          dev_ctx.x_context(), input_ptr + i * im2col_step * input_dim,
+          filter_ptr, offset_ptr + i * im2col_step * input_offset_dim,
+          mask_ptr + i * im2col_step * input_mask_dim,
+          output_prt + i * im2col_step * output_dim, n, c, h, w, f, ksize,
+          strides, paddings, dilations, groups, deformable_groups, nullptr,
+          nullptr, nullptr, true);
+      PADDLE_ENFORCE_EQ(
+          r, XPU_SUCCESS,
+          platform::errors::External(
+              "XPU deformable_conv kernel return wrong value[%d].", r));
+    }
+  }
+};
+
+template <typename DeviceContext, typename T>
+class DeformableConvGradXPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    const Tensor* output_grad =
+        ctx.Input<Tensor>(framework::GradVarName("Output"));
+    Tensor* input_grad = ctx.Output<Tensor>(framework::GradVarName("Input"));
+    Tensor* filter_grad = ctx.Output<Tensor>(framework::GradVarName("Filter"));
+    Tensor* offset_grad = ctx.Output<Tensor>(framework::GradVarName("Offset"));
+    Tensor* mask_grad = ctx.Output<Tensor>(framework::GradVarName("Mask"));
+    T* dx_data = nullptr;
+    T* dw_data = nullptr;
+    T* dmask_data = nullptr;
+    T* doffset_data = nullptr;
+
+    if (input_grad != nullptr) {
+      input_grad->mutable_data<T>(ctx.GetPlace());
+      dx_data = input_grad->data<T>();
+    }
+    if (filter_grad != nullptr) {
+      filter_grad->mutable_data<T>(ctx.GetPlace());
+      dw_data = filter_grad->data<T>();
+    }
+    if (offset_grad != nullptr) {
+      offset_grad->mutable_data<T>(ctx.GetPlace());
+      doffset_data = offset_grad->data<T>();
+    }
+    if (mask_grad != nullptr) {
+      mask_grad->mutable_data<T>(ctx.GetPlace());
+      dmask_data = mask_grad->data<T>();
+    }
+
+    const Tensor* input = ctx.Input<Tensor>("Input");
+    Tensor offset = *ctx.Input<Tensor>("Offset");
+    Tensor mask = *ctx.Input<Tensor>("Mask");
+    Tensor filter = *ctx.Input<Tensor>("Filter");
+
+    int groups = ctx.Attr<int>("groups");
+    int deformable_groups = ctx.Attr<int>("deformable_groups");
+    int im2col_step = ctx.Attr<int>("im2col_step");
+    std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
+    std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
+    std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
+
+    PADDLE_ENFORCE_EQ(
+        deformable_groups == 1, true,
+        platform::errors::InvalidArgument((
+            "XPU only support deformable_groups == 1 in deformable_conv op.")));
+    PADDLE_ENFORCE_EQ(
+        groups == 1, true,
+        platform::errors::InvalidArgument(
+            ("XPU only support groups == 1 in deformable_conv op.")));
+    PADDLE_ENFORCE_EQ(filter.dims()[2] <= 8 && filter.dims()[3] <= 8, true,
+                      platform::errors::InvalidArgument(
+                          "Filter high and weight should less than 8 on xpu "
+                          "in deformable_conv op."));
+
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+    const int batch_size = static_cast<int>(input->dims()[0]);
+    std::vector<int64_t> output_shape_vec(
+        framework::vectorize(output_grad->dims()));
+    const T* output_grad_ptr = output_grad->data<T>();
+    const T* input_ptr = input->data<T>();
+    const T* filter_ptr = filter.data<T>();
+    const float* offset_ptr = offset.data<float>();
+    const float* mask_ptr = mask.data<float>();
+    if (dx_data == nullptr) {
+      PADDLE_ENFORCE_EQ(xpu_malloc(reinterpret_cast<void**>(&dx_data),
+                                   input->numel() * sizeof(T)),
+                        XPU_SUCCESS, platform::errors::ResourceExhausted(
+                                         "XPU has no enough memory"));
+    }
+    if (dw_data == nullptr) {
+      PADDLE_ENFORCE_EQ(xpu_malloc(reinterpret_cast<void**>(&dw_data),
+                                   filter.numel() * sizeof(T)),
+                        XPU_SUCCESS, platform::errors::ResourceExhausted(
+                                         "XPU has no enough memory"));
+    }
+    if (doffset_data == nullptr) {
+      PADDLE_ENFORCE_EQ(xpu_malloc(reinterpret_cast<void**>(&doffset_data),
+                                   offset.numel() * sizeof(T)),
+                        XPU_SUCCESS, platform::errors::ResourceExhausted(
+                                         "XPU has no enough memory"));
+    }
+    if (dmask_data == nullptr) {
+      PADDLE_ENFORCE_EQ(xpu_malloc(reinterpret_cast<void**>(&dmask_data),
+                                   mask.numel() * sizeof(T)),
+                        XPU_SUCCESS, platform::errors::ResourceExhausted(
+                                         "XPU has no enough memory"));
+    }
+
+    int input_dim = input->numel() / input->dims()[0];
+    int input_offset_dim = offset.numel() / offset.dims()[0];
+    int input_mask_dim = mask.numel() / mask.dims()[0];
+    int output_dim =
+        output_shape_vec[1] * output_shape_vec[2] * output_shape_vec[3];
+    std::vector<int> ksize{static_cast<int>(filter.dims()[2]),
+                           static_cast<int>(filter.dims()[3])};
+    int n = im2col_step;
+    int c = input->dims()[1];
+    int h = input->dims()[2];
+    int w = input->dims()[3];
+    int f = filter.dims()[0];
+
+    T* filter_grad_tmp = nullptr;
+    PADDLE_ENFORCE_EQ(xpu_malloc(reinterpret_cast<void**>(&filter_grad_tmp),
+                                 filter_grad->numel() * sizeof(T)),
+                      XPU_SUCCESS, platform::errors::ResourceExhausted(
+                                       "XPU has no enough memory"));
+
+    // set zeros for d_table_data
+    const int zero = 0;
+    int r_dx =
+        xpu::constant<T>(dev_ctx.x_context(), dx_data, input->numel(), zero);
+    int r_dw =
+        xpu::constant<T>(dev_ctx.x_context(), dw_data, filter.numel(), zero);
+    int r_doffset = xpu::constant<T>(dev_ctx.x_context(), doffset_data,
+                                     offset.numel(), zero);
+    int r_dmask =
+        xpu::constant<T>(dev_ctx.x_context(), dmask_data, mask.numel(), zero);
+    int r_filter = xpu::constant<T>(dev_ctx.x_context(), filter_grad_tmp,
+                                    filter.numel(), zero);
+    auto ret = (r_dx == xpu::Error_t::SUCCESS) && (r_dx == r_dw) &&
+               (r_dx == r_doffset) && (r_dx == r_dmask) && (r_dx == r_filter);
+    PADDLE_ENFORCE_EQ(ret, true,
+                      platform::errors::External(
+                          "XPU API return wrong value, please check where "
+                          "Baidu Kunlun Card is properly installed."));
+
+    for (int i = 0; i < batch_size / im2col_step; ++i) {
+      int r = xpu::deformable_conv_grad<float, float, float, int>(
+          dev_ctx.x_context(), input_ptr + i * im2col_step * input_dim,
+          filter_ptr, offset_ptr + i * im2col_step * input_offset_dim,
+          mask_ptr + i * im2col_step * input_mask_dim,
+          output_grad_ptr + i * im2col_step * output_dim,
+          dx_data + i * im2col_step * input_dim, filter_grad_tmp,
+          doffset_data + i * im2col_step * input_offset_dim,
+          dmask_data + i * im2col_step * input_mask_dim, n, c, h, w, f, ksize,
+          strides, paddings, dilations, groups, deformable_groups, nullptr,
+          nullptr, nullptr, nullptr, nullptr, true);
+      PADDLE_ENFORCE_EQ(
+          r, XPU_SUCCESS,
+          platform::errors::External(
+              "XPU deformable_conv_grad kernel return wrong value[%d].", r));
+      r = baidu::xpu::api::add<T>(dev_ctx.x_context(), filter_grad_tmp, dw_data,
+                                  dw_data, filter.numel());
+      PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
+                        platform::errors::External(
+                            "XPU add kernel return wrong value[%d].", r));
+    }
+
+    dev_ctx.Wait();
+    xpu_free(filter_grad_tmp);
+    if (input_grad == nullptr) {
+      xpu_free(dx_data);
+    }
+    if (filter_grad == nullptr) {
+      xpu_free(dw_data);
+    }
+    if (offset_grad == nullptr) {
+      xpu_free(doffset_data);
+    }
+    if (mask_grad == nullptr) {
+      xpu_free(dmask_data);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+using XPUDeviceContext = paddle::platform::XPUDeviceContext;
+
+REGISTER_OP_XPU_KERNEL(deformable_conv,
+                       ops::DeformableConvXPUKernel<XPUDeviceContext, float>);
+REGISTER_OP_XPU_KERNEL(
+    deformable_conv_grad,
+    ops::DeformableConvGradXPUKernel<XPUDeviceContext, float>);
+
+#endif
diff --git a/python/paddle/fluid/tests/unittests/xpu/test_deformable_conv_op_xpu.py b/python/paddle/fluid/tests/unittests/xpu/test_deformable_conv_op_xpu.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c611b629988884e07d9593de7f89a6121a119a6
--- /dev/null
+++ b/python/paddle/fluid/tests/unittests/xpu/test_deformable_conv_op_xpu.py
@@ -0,0 +1,274 @@
+#   Copyright (c) 2019 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 sys
+sys.path.append("..")
+import unittest
+import numpy as np
+
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+from op_test_xpu import OpTest, XPUOpTest
+import paddle
+from paddle.fluid import Program, program_guard
+
+
+def dmc_bilinear(data_im, height, width, h, w):
+    h_low = int(np.floor(h))
+    w_low = int(np.floor(w))
+    h_high = h_low + 1
+    w_high = w_low + 1
+
+    lh = h - h_low
+    lw = w - w_low
+    hh = 1 - lh
+    hw = 1 - lw
+
+    v1 = 0
+    if h_low >= 0 and w_low >= 0:
+        v1 = data_im[h_low, w_low]
+    v2 = 0
+    if h_low >= 0 and w_high <= width - 1:
+        v2 = data_im[h_low, w_high]
+    v3 = 0
+    if h_high <= height - 1 and w_low >= 0:
+        v3 = data_im[h_high, w_low]
+    v4 = 0
+    if h_high <= height - 1 and w_high <= width - 1:
+        v4 = data_im[h_high, w_high]
+
+    w1, w2, w3, w4 = hh * hw, hh * lw, lh * hw, lh * lw
+    val = w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4
+
+    return val
+
+
+def dconv_im2col_gemm(input, offset, mask, filter, group, conv_param):
+    in_n, in_c, in_h, in_w = input.shape
+    out_c, f_c, f_h, f_w = filter.shape
+
+    assert offset.shape == (in_n, 2 * f_h * f_w, in_h, in_w)
+    assert mask.shape == (in_n, f_h * f_w, in_h, in_w)
+    assert f_c * group == in_c
+    assert np.mod(out_c, group) == 0
+
+    stride, pad, dilation = conv_param['stride'], conv_param['pad'],\
+        conv_param['dilation']
+    out_h = 1 + (in_h + 2 * pad[0] - (dilation[0] * (f_h - 1) + 1)) // stride[0]
+    out_w = 1 + (in_w + 2 * pad[1] - (dilation[1] * (f_w - 1) + 1)) // stride[1]
+    assert out_h == in_h
+    assert out_w == in_w
+
+    col_buffer = np.zeros((in_n, in_c * f_h * f_w, in_h * in_w))
+    for n in range(in_n):
+        for c in range(in_c):
+            for h in range(out_h):
+                for w in range(out_w):
+                    for kh in range(f_h):
+                        for kw in range(f_w):
+                            offset_h_table = \
+                                    offset[n, ::2, h, w].reshape(f_h, f_w)
+                            offset_w_table = \
+                                    offset[n, 1::2, h, w].reshape(f_h, f_w)
+                            mask_table = \
+                                mask[n, :, h, w].reshape(f_h, f_w)
+                            offset_h = offset_h_table[kh, kw]
+                            offset_w = offset_w_table[kh, kw]
+                            val = 0
+                            im_h = h * stride[0] + kh * dilation[0] \
+                                + offset_h - pad[0]
+                            im_w = w * stride[0] + kw * dilation[0] \
+                                + offset_w - pad[1]
+                            if im_h > -1 and im_w > -1 and \
+                                im_h < in_h and im_w < in_h:
+                                val = dmc_bilinear(input[n, c], in_h, in_w,
+                                                   im_h, im_w)
+                            val_out = val * mask_table[kh, kw]
+                            col_buffer[n, c * f_h * f_w + kh * f_w + kw, h *
+                                       in_w + w] = val_out
+
+    out = np.zeros((in_n, group, int(out_c // group), out_h * out_w))
+    weight = filter.reshape(group, int(out_c // group), f_c * f_h * f_w)
+    col_buffer = col_buffer.reshape(
+        (in_n, group, int(in_c // group * f_h * f_w), in_h * in_w))
+    for n in range(in_n):
+        for g in range(group):
+            out[n, g] = np.matmul(weight[g], col_buffer[n, g])
+    out = out.reshape(in_n, out_c, out_h, out_w)
+    return out
+
+
+class TestModulatedDeformableConvOp(XPUOpTest):
+    def setUp(self):
+        self.op_type = "deformable_conv"
+        self.dtype = np.float32
+        self.init_group()
+        self.init_dilation()
+        self.init_test_case()
+
+        conv_param = {
+            'stride': self.stride,
+            'pad': self.pad,
+            'dilation': self.dilations
+        }
+
+        input = np.random.random(self.input_size).astype(self.dtype)
+        offset = 10 * np.random.random(self.offset_size).astype(self.dtype)
+        mask = 10 * np.random.random(self.mask_size).astype(self.dtype)
+        filter = np.random.random(self.filter_size).astype(self.dtype)
+        output = dconv_im2col_gemm(input, offset, mask, filter, self.groups,
+                                   conv_param)
+        output = output.astype(self.dtype)
+
+        self.inputs = {
+            'Input': OpTest.np_dtype_to_fluid_dtype(input),
+            'Offset': OpTest.np_dtype_to_fluid_dtype(offset),
+            'Mask': OpTest.np_dtype_to_fluid_dtype(mask),
+            'Filter': OpTest.np_dtype_to_fluid_dtype(filter)
+        }
+        self.attrs = {
+            'strides': self.stride,
+            'paddings': self.pad,
+            'groups': self.groups,
+            'deformable_groups': self.deformable_groups,
+            'im2col_step': self.im2col_step,
+            'dilations': self.dilations,
+        }
+        self.outputs = {'Output': output}
+
+    def has_cuda(self):
+        return core.is_compiled_with_cuda() and (self.use_cudnn or
+                                                 self.use_cuda)
+
+    def test_check_output(self):
+        if core.is_compiled_with_xpu():
+            paddle.enable_static()
+            place = paddle.XPUPlace(0)
+            self.check_output_with_place(place)
+
+    def test_check_grad(self):
+        if core.is_compiled_with_xpu():
+            paddle.enable_static()
+            place = paddle.XPUPlace(0)
+            self.check_grad_with_place(
+                place, {'Input', 'Offset', 'Mask', 'Filter'},
+                'Output',
+                max_relative_error=0.06)
+
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.input_size = [2, 8, 4, 4]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [8, f_c, 3, 3]
+        self.im2col_step = 1
+        self.deformable_groups = 1
+        offset_c = 2 * self.deformable_groups * self.filter_size[
+            2] * self.filter_size[3]
+        mask_c = self.deformable_groups * self.filter_size[
+            2] * self.filter_size[3]
+        self.offset_size = [
+            self.input_size[0], offset_c, self.input_size[2], self.input_size[3]
+        ]
+        self.mask_size = [
+            self.input_size[0], mask_c, self.input_size[2], self.input_size[3]
+        ]
+
+    def init_dilation(self):
+        self.dilations = [1, 1]
+
+    def init_group(self):
+        self.groups = 1
+
+
+class TestWithDilation(TestModulatedDeformableConvOp):
+    def init_test_case(self):
+        self.pad = [2, 2]
+        self.stride = [1, 1]
+        self.input_size = [4, 3, 4, 4]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [6, f_c, 3, 3]
+        self.im2col_step = 1
+        self.deformable_groups = 1
+        offset_c = 2 * self.deformable_groups * self.filter_size[
+            2] * self.filter_size[3]
+        mask_c = self.deformable_groups * self.filter_size[
+            2] * self.filter_size[3]
+        self.offset_size = [
+            self.input_size[0], offset_c, self.input_size[2], self.input_size[3]
+        ]
+        self.mask_size = [
+            self.input_size[0], mask_c, self.input_size[2], self.input_size[3]
+        ]
+
+    def init_dilation(self):
+        self.dilations = [2, 2]
+
+
+class TestWith3x3(TestModulatedDeformableConvOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [6, f_c, 3, 3]
+        self.im2col_step = 1
+        self.deformable_groups = 1
+        offset_c = 2 * self.deformable_groups * self.filter_size[
+            2] * self.filter_size[3]
+        mask_c = self.deformable_groups * self.filter_size[
+            2] * self.filter_size[3]
+        self.offset_size = [
+            self.input_size[0], offset_c, self.input_size[2], self.input_size[3]
+        ]
+        self.mask_size = [
+            self.input_size[0], mask_c, self.input_size[2], self.input_size[3]
+        ]
+
+
+class TestModulatedDeformableConvInvalidInput(unittest.TestCase):
+    def test_error(self):
+        def test_invalid_input():
+            paddle.enable_static()
+            input = [1, 3, 32, 32]
+            offset = fluid.data(
+                name='offset', shape=[None, 3, 32, 32], dtype='float32')
+            mask = fluid.data(
+                name='mask', shape=[None, 3, 32, 32], dtype='float32')
+            loss = fluid.layers.deformable_conv(
+                input, offset, mask, num_filters=4, filter_size=1)
+
+        self.assertRaises(TypeError, test_invalid_input)
+
+        def test_invalid_offset():
+            paddle.enable_static()
+            input = fluid.data(
+                name='input', shape=[None, 3, 32, 32], dtype='int32')
+            offset = fluid.data(
+                name='offset', shape=[None, 3, 32, 32], dtype='float32')
+            mask = fluid.data(
+                name='mask', shape=[None, 3, 32, 32], dtype='float32')
+            loss = fluid.layers.deformable_conv(
+                input, offset, mask, num_filters=4, filter_size=1)
+
+        self.assertRaises(TypeError, test_invalid_offset)
+
+
+if __name__ == '__main__':
+    unittest.main()