diff --git a/paddle/fluid/platform/device/ipu/popart_canonicalization/detection_ops.cc b/paddle/fluid/platform/device/ipu/popart_canonicalization/detection_ops.cc
new file mode 100644
index 0000000000000000000000000000000000000000..159e1dfc1493a5cb0a878e3e6c4cecfa49bb1ce7
--- /dev/null
+++ b/paddle/fluid/platform/device/ipu/popart_canonicalization/detection_ops.cc
@@ -0,0 +1,444 @@
+// 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/platform/device/ipu/popart_canonicalization/canonicalization_utils.h"
+#include "paddle/fluid/platform/device/ipu/popart_canonicalization/op_builder.h"
+#include "paddle/fluid/platform/enforce.h"
+
+namespace paddle {
+namespace platform {
+namespace ipu {
+namespace {
+
+Node *yolo_box_handler(Graph *graph, Node *node) {
+  auto *op = node->Op();
+  auto clip_bbox = PADDLE_GET_CONST(bool, op->GetAttr("clip_bbox"));
+  auto iou_aware = PADDLE_GET_CONST(bool, op->GetAttr("iou_aware"));
+  auto conf_thresh = PADDLE_GET_CONST(float, op->GetAttr("conf_thresh"));
+  auto iou_aware_factor =
+      PADDLE_GET_CONST(float, op->GetAttr("iou_aware_factor"));
+  auto class_num = PADDLE_GET_CONST(int, op->GetAttr("class_num"));
+  auto downsample_ratio =
+      PADDLE_GET_CONST(int, op->GetAttr("downsample_ratio"));
+  auto scale_x_y = PADDLE_GET_CONST(float, op->GetAttr("scale_x_y"));
+  auto anchors = PADDLE_GET_CONST(std::vector<int>, op->GetAttr("anchors"));
+
+  // For Slice Op, while value is very large, it equals to the ends.
+  int max_int = INT_MAX;
+  int anchor_num = anchors.size() / 2;
+
+  // FP32 or FP16
+  auto target_dtype = GetInputVarNode("X", node)->Var()->GetDataType();
+
+  Node *input_x = GetInputVarNode("X", node);
+  if (iou_aware) {
+    input_x =
+        CreateSlice(graph,
+                    node,
+                    {input_x},
+                    {},
+                    std::vector<int>{0, 0, 0, 0},
+                    std::vector<int>{max_int, anchor_num, max_int, max_int},
+                    std::vector<int>{0, 1, 2, 3},
+                    std::vector<int>{1, 1, 1, 1})
+            ->outputs[0];
+  }
+  auto nchw = GetInputVarNode("X", node)->Var()->GetShape();
+  // Channel `C` = anchor_num * (5 + class_num)
+  auto *reshaped_x =
+      CreateReshape(
+          graph,
+          node,
+          {input_x},
+          {},
+          std::vector<int64_t>{nchw[0], anchor_num, -1, nchw[2], nchw[3]})
+          ->outputs[0];
+  auto *transposed_x =
+      CreateBaseOp(graph,
+                   node,
+                   "popart_transpose",
+                   {reshaped_x},
+                   {},
+                   {{"perm", std::vector<int64_t>{0, 1, 3, 4, 2}}})
+          ->outputs[0];
+
+  // Build the grid
+  // grid_x_0 shape is [w]
+  std::vector<float> grid_x_0(nchw[3]);
+  std::iota(grid_x_0.begin(), grid_x_0.end(), 0.0f);
+  // grid_y_0 shape is [h]
+  std::vector<float> grid_y_0(nchw[2]);
+  std::iota(grid_y_0.begin(), grid_y_0.end(), 0.0f);
+  // grid_x_1 shape is [w * h]
+  std::vector<float> grid_x_1;
+  for (int i = 0; i < nchw[2]; i++) {
+    grid_x_1.insert(grid_x_1.end(), grid_x_0.begin(), grid_x_0.end());
+  }
+  auto *grid_x_1_node = CreateConst(graph,
+                                    node,
+                                    grid_x_1,
+                                    {int64_t(grid_x_1.size())},
+                                    VarType2OnnxDType(target_dtype))
+                            ->outputs[0];
+  // grid_y_1 shape is [h * w]
+  std::vector<float> grid_y_1;
+  for (int i = 0; i < nchw[3]; i++) {
+    grid_y_1.insert(grid_y_1.end(), grid_y_0.begin(), grid_y_0.end());
+  }
+  auto *grid_y_1_node = CreateConst(graph,
+                                    node,
+                                    grid_y_1,
+                                    {int64_t(grid_y_1.size())},
+                                    VarType2OnnxDType(target_dtype))
+                            ->outputs[0];
+  auto *grid_x_node = CreateReshape(graph,
+                                    node,
+                                    {grid_x_1_node},
+                                    {},
+                                    std::vector<int64_t>{nchw[2], nchw[3], 1})
+                          ->outputs[0];
+  auto *grid_y_2_node = CreateReshape(graph,
+                                      node,
+                                      {grid_y_1_node},
+                                      {},
+                                      std::vector<int64_t>{nchw[3], nchw[2], 1})
+                            ->outputs[0];
+  auto *grid_y_node = CreateBaseOp(graph,
+                                   node,
+                                   "popart_transpose",
+                                   {grid_y_2_node},
+                                   {},
+                                   {{"perm", std::vector<int64_t>{1, 0, 2}}})
+                          ->outputs[0];
+  auto *grid_node = CreateBaseOp(graph,
+                                 node,
+                                 "popart_concat",
+                                 {grid_x_node, grid_y_node},
+                                 {},
+                                 {{"axis", int64_t(2)}})
+                        ->outputs[0];
+
+  // Generate the positions(x, y) of boxes
+  // pred_box[:, :, :, :, 0] = (grid_x + sigmoid(pred_box[:, :, :, :, 0]) *
+  // scale_x_y + bias_x_y) / w pred_box[:, :, :, :, 1] = (grid_y +
+  // sigmoid(pred_box[:, :, :, :, 1]) * scale_x_y + bias_x_y) / h
+  auto *pred_box_xy =
+      CreateSlice(graph,
+                  node,
+                  {transposed_x},
+                  {},
+                  std::vector<int>{0, 0, 0, 0, 0},
+                  std::vector<int>{max_int, max_int, max_int, max_int, 2},
+                  std::vector<int>{0, 1, 2, 3, 4},
+                  std::vector<int>{1, 1, 1, 1, 1})
+          ->outputs[0];
+  auto *scale_x_y_node = CreateConst(graph,
+                                     node,
+                                     std::vector<float>{scale_x_y},
+                                     {int64_t(1)},
+                                     VarType2OnnxDType(target_dtype))
+                             ->outputs[0];
+  auto *bias_x_y_node =
+      CreateConst(graph,
+                  node,
+                  std::vector<float>{(1.0f - scale_x_y) / 2.0f},
+                  {int64_t(1)},
+                  VarType2OnnxDType(target_dtype))
+          ->outputs[0];
+  auto *wh = CreateConst(graph,
+                         node,
+                         std::vector<float>{static_cast<float>(nchw[3]),
+                                            static_cast<float>(nchw[2])},
+                         {int64_t(2)},
+                         VarType2OnnxDType(target_dtype))
+                 ->outputs[0];
+  pred_box_xy = CreateBaseOp(graph, node, "popart_sigmoid", {pred_box_xy}, {})
+                    ->outputs[0];
+  pred_box_xy =
+      CreateBaseOp(graph, node, "popart_mul", {pred_box_xy, scale_x_y_node}, {})
+          ->outputs[0];
+  pred_box_xy =
+      CreateBaseOp(graph, node, "popart_add", {pred_box_xy, bias_x_y_node}, {})
+          ->outputs[0];
+  pred_box_xy =
+      CreateBaseOp(graph, node, "popart_add", {pred_box_xy, grid_node}, {})
+          ->outputs[0];
+  pred_box_xy = CreateBaseOp(graph, node, "popart_div", {pred_box_xy, wh}, {})
+                    ->outputs[0];
+
+  // Generate Width and Height of boxes
+  // anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]
+  // anchors_s = np.array(
+  //     [(an_w / input_w, an_h / input_h) for an_w, an_h in anchors])
+  // anchor_w = anchors_s[:, 0:1].reshape((1, an_num, 1, 1))
+  // anchor_h = anchors_s[:, 1:2].reshape((1, an_num, 1, 1))
+  auto *anchors_node =
+      CreateConst(
+          graph,
+          node,
+          std::vector<float>{anchors.begin(), anchors.begin() + anchor_num * 2},
+          {int64_t(anchor_num * 2)},
+          VarType2OnnxDType(target_dtype))
+          ->outputs[0];
+  anchors_node =
+      CreateReshape(
+          graph, node, {anchors_node}, {}, std::vector<int64_t>{anchor_num, 2})
+          ->outputs[0];
+  auto *downsample_node =
+      CreateConst(graph,
+                  node,
+                  std::vector<float>{static_cast<float>(downsample_ratio)},
+                  {int64_t(1)},
+                  VarType2OnnxDType(target_dtype))
+          ->outputs[0];
+  auto *ori_wh =
+      CreateBaseOp(graph, node, "popart_mul", {wh, downsample_node}, {})
+          ->outputs[0];
+  anchors_node =
+      CreateBaseOp(graph, node, "popart_div", {anchors_node, ori_wh}, {})
+          ->outputs[0];
+  anchors_node = CreateReshape(graph,
+                               node,
+                               {anchors_node},
+                               {},
+                               std::vector<int64_t>{1, anchor_num, 1, 1, 2})
+                     ->outputs[0];
+  auto *pred_box_wh =
+      CreateSlice(graph,
+                  node,
+                  {transposed_x},
+                  {},
+                  std::vector<int>{0, 0, 0, 0, 2},
+                  std::vector<int>{max_int, max_int, max_int, max_int, 4},
+                  std::vector<int>{0, 1, 2, 3, 4},
+                  std::vector<int>{1, 1, 1, 1, 1})
+          ->outputs[0];
+  pred_box_wh =
+      CreateBaseOp(graph, node, "popart_exp", {pred_box_wh}, {})->outputs[0];
+  pred_box_wh =
+      CreateBaseOp(graph, node, "popart_mul", {pred_box_wh, anchors_node}, {})
+          ->outputs[0];
+
+  // Ignore the boxes whose confidience lower than the threshold
+  // if iou_aware:
+  //     pred_conf = sigmoid(x[:, :, :, :, 4:5])**(
+  //         1 - iou_aware_factor) * sigmoid(ioup)**iou_aware_factor
+  // else:
+  //     pred_conf = sigmoid(x[:, :, :, :, 4:5])
+  auto *confidence =
+      CreateSlice(graph,
+                  node,
+                  {transposed_x},
+                  {},
+                  std::vector<int>{0, 0, 0, 0, 4},
+                  std::vector<int>{max_int, max_int, max_int, max_int, 5},
+                  std::vector<int>{0, 1, 2, 3, 4},
+                  std::vector<int>{1, 1, 1, 1, 1})
+          ->outputs[0];
+  auto *pred_conf =
+      CreateBaseOp(graph, node, "popart_sigmoid", {confidence}, {})->outputs[0];
+  if (iou_aware) {
+    auto *ioup =
+        CreateSlice(graph,
+                    node,
+                    {GetInputVarNode("X", node)},
+                    {},
+                    std::vector<int>{0, 0, 0, 0},
+                    std::vector<int>{max_int, anchor_num, max_int, max_int},
+                    std::vector<int>{0, 1, 2, 3},
+                    std::vector<int>{1, 1, 1, 1})
+            ->outputs[0];
+    ioup = CreateBaseOp(graph,
+                        node,
+                        "popart_unsqueeze",
+                        {ioup},
+                        {},
+                        {{"axes", std::vector<int64_t>{4}}})
+               ->outputs[0];
+    ioup = CreateBaseOp(graph, node, "popart_sigmoid", {ioup}, {})->outputs[0];
+    auto *power_0 = CreateConst(graph,
+                                node,
+                                std::vector<float>{1.0f - iou_aware_factor},
+                                {int64_t(1)},
+                                VarType2OnnxDType(target_dtype))
+                        ->outputs[0];
+    auto *power_1 = CreateConst(graph,
+                                node,
+                                std::vector<float>{iou_aware_factor},
+                                {int64_t(1)},
+                                VarType2OnnxDType(target_dtype))
+                        ->outputs[0];
+    ioup = CreateBaseOp(graph, node, "popart_pow", {ioup, power_1}, {})
+               ->outputs[0];
+    pred_conf =
+        CreateBaseOp(graph, node, "popart_pow", {pred_conf, power_0}, {})
+            ->outputs[0];
+    pred_conf = CreateBaseOp(graph, node, "popart_mul", {pred_conf, ioup}, {})
+                    ->outputs[0];
+  }
+  // pred_conf[pred_conf < conf_thresh] = 0.
+  // pred_score = sigmoid(x[:, :, :, :, 5:]) * pred_conf
+  // pred_box = pred_box * (pred_conf > 0.).astype('float32')
+  auto *value_2 = CreateConst(graph,
+                              node,
+                              std::vector<float>{2.0f},
+                              {int64_t(1)},
+                              VarType2OnnxDType(target_dtype))
+                      ->outputs[0];
+  auto *center =
+      CreateBaseOp(graph, node, "popart_div", {pred_box_wh, value_2}, {})
+          ->outputs[0];
+  auto *min_xy =
+      CreateBaseOp(graph, node, "popart_sub", {pred_box_xy, center}, {})
+          ->outputs[0];
+  auto *max_xy =
+      CreateBaseOp(graph, node, "popart_add", {pred_box_xy, center}, {})
+          ->outputs[0];
+
+  auto *conf_thresh_node = CreateConst(graph,
+                                       node,
+                                       std::vector<float>{conf_thresh},
+                                       {int64_t(1)},
+                                       VarType2OnnxDType(target_dtype))
+                               ->outputs[0];
+  auto *filter =
+      CreateBaseOp(
+          graph, node, "popart_greater", {pred_conf, conf_thresh_node}, {})
+          ->outputs[0];
+  filter = CreateCast(graph, node, {filter}, {}, target_dtype)->outputs[0];
+  pred_conf = CreateBaseOp(graph, node, "popart_mul", {pred_conf, filter}, {})
+                  ->outputs[0];
+  auto *pred_score =
+      CreateSlice(graph,
+                  node,
+                  {transposed_x},
+                  {},
+                  std::vector<int>{0, 0, 0, 0, 5},
+                  std::vector<int>{max_int, max_int, max_int, max_int, max_int},
+                  std::vector<int>{0, 1, 2, 3, 4},
+                  std::vector<int>{1, 1, 1, 1, 1})
+          ->outputs[0];
+  pred_score =
+      CreateBaseOp(graph, node, "popart_sigmoid", {pred_score}, {})->outputs[0];
+  pred_score =
+      CreateBaseOp(graph, node, "popart_mul", {pred_score, pred_conf}, {})
+          ->outputs[0];
+  auto *pred_box = CreateBaseOp(graph,
+                                node,
+                                "popart_concat",
+                                {min_xy, max_xy},
+                                {},
+                                {{"axis", int64_t(4)}})
+                       ->outputs[0];
+  pred_box = CreateBaseOp(graph, node, "popart_mul", {pred_box, filter}, {})
+                 ->outputs[0];
+  pred_box =
+      CreateReshape(
+          graph, node, {pred_box}, {}, std::vector<int64_t>{nchw[0], -1, 4})
+          ->outputs[0];
+
+  // Clip the boxes to img_size
+  auto *float_img_size =
+      CreateCast(
+          graph, node, {GetInputVarNode("ImgSize", node)}, {}, target_dtype)
+          ->outputs[0];
+  float_img_size = CreateBaseOp(graph,
+                                node,
+                                "popart_unsqueeze",
+                                {float_img_size},
+                                {},
+                                {{"axes", std::vector<int64_t>(1)}})
+                       ->outputs[0];
+  auto split_im_hw =
+      CreateSplit(
+          graph, node, {float_img_size}, {}, std::vector<int64_t>{1, 1}, 2)
+          ->outputs;
+  auto *im_whwh =
+      CreateBaseOp(
+          graph,
+          node,
+          "popart_concat",
+          {split_im_hw[1], split_im_hw[0], split_im_hw[1], split_im_hw[0]},
+          {},
+          {{"axis", int64_t(2)}})
+          ->outputs[0];
+  if (!clip_bbox) {
+    auto *out = CreateBaseOp(graph, node, "popart_mul", {pred_box, im_whwh}, {})
+                    ->outputs[0];
+    CreateCast(graph,
+               node,
+               {out},
+               {GetOutputVarNode("Boxes", node)},
+               GetOutputVarNode("Boxes", node)->Var()->GetDataType());
+
+  } else {
+    pred_box = CreateBaseOp(graph, node, "popart_mul", {pred_box, im_whwh}, {})
+                   ->outputs[0];
+    auto *im_wh = CreateBaseOp(graph,
+                               node,
+                               "popart_concat",
+                               {split_im_hw[1], split_im_hw[0]},
+                               {},
+                               {{"axis", int64_t(2)}})
+                      ->outputs[0];
+    auto *float_value_1 = CreateConst(graph,
+                                      node,
+                                      std::vector<float>{1.0f},
+                                      {int64_t(1)},
+                                      VarType2OnnxDType(target_dtype))
+                              ->outputs[0];
+    im_wh = CreateBaseOp(graph, node, "popart_sub", {im_wh, float_value_1}, {})
+                ->outputs[0];
+    auto pred_box_xymin_xymax =
+        CreateSplit(graph, node, {pred_box}, {}, std::vector<int64_t>{2, 2}, 2)
+            ->outputs;
+    pred_box_xymin_xymax[0] =
+        CreateBaseOp(graph, node, "popart_relu", {pred_box_xymin_xymax[0]}, {})
+            ->outputs[0];
+    pred_box_xymin_xymax[1] =
+        CreateBaseOp(
+            graph, node, "popart_min", {pred_box_xymin_xymax[1], im_wh}, {})
+            ->outputs[0];
+    auto *out = CreateBaseOp(graph,
+                             node,
+                             "popart_concat",
+                             pred_box_xymin_xymax,
+                             {},
+                             {{"axis", int64_t(2)}})
+                    ->outputs[0];
+    CreateCast(graph,
+               node,
+               {out},
+               {GetOutputVarNode("Boxes", node)},
+               GetOutputVarNode("Boxes", node)->Var()->GetDataType());
+  }
+  auto *score_out = CreateReshape(graph,
+                                  node,
+                                  {pred_score},
+                                  {},
+                                  std::vector<int64_t>{nchw[0], -1, class_num})
+                        ->outputs[0];
+  return CreateCast(graph,
+                    node,
+                    {score_out},
+                    {GetOutputVarNode("Scores", node)},
+                    GetOutputVarNode("Scores", node)->Var()->GetDataType());
+}
+
+}  // namespace
+}  // namespace ipu
+}  // namespace platform
+}  // namespace paddle
+
+REGISTER_HANDLER(yolo_box, yolo_box_handler);
diff --git a/paddle/fluid/platform/device/ipu/popart_canonicalization/nn_ops.cc b/paddle/fluid/platform/device/ipu/popart_canonicalization/nn_ops.cc
index e28531f349d1436de8c90ee0270f21012474e29f..a016647efc99749b7b3597174654e6666e10b67b 100644
--- a/paddle/fluid/platform/device/ipu/popart_canonicalization/nn_ops.cc
+++ b/paddle/fluid/platform/device/ipu/popart_canonicalization/nn_ops.cc
@@ -656,30 +656,14 @@ Node *interp_handler(Graph *graph, Node *node, const std::string &mode) {
         CreateBaseOp(
             graph, node, "popart_shape", {GetInputVarNode("X", node)}, {})
             ->outputs[0];
-    Node *start = CreateConst(graph,
-                              node,
-                              std::vector<int>{0},
-                              std::vector<int64_t>{1},
-                              ONNXDataType::INT32)
-                      ->outputs[0];
-    Node *end = CreateConst(graph,
-                            node,
-                            std::vector<int>{2},
-                            std::vector<int64_t>{1},
-                            ONNXDataType::INT32)
-                    ->outputs[0];
-    Node *axes = CreateConst(graph,
-                             node,
-                             std::vector<int>{0},
-                             std::vector<int64_t>{1},
-                             ONNXDataType::INT32)
-                     ->outputs[0];
-    Node *nc = CreateBaseOp(graph,
-                            node,
-                            "popart_slice",
-                            {input_shape, start, end, axes},
-                            {},
-                            {})
+    Node *nc = CreateSlice(graph,
+                           node,
+                           {input_shape},
+                           {},
+                           std::vector<int>{0},
+                           std::vector<int>{2},
+                           std::vector<int>{0},
+                           std::vector<int>{1})
                    ->outputs[0];
     size = CreateBaseOp(graph,
                         node,
diff --git a/paddle/fluid/platform/device/ipu/popart_canonicalization/op_builder.cc b/paddle/fluid/platform/device/ipu/popart_canonicalization/op_builder.cc
index 6badf37d5b334ef5f999ff572e17ef4c53be0941..ceaf21377c2b01130b932f7c775ae3e8f2866465 100644
--- a/paddle/fluid/platform/device/ipu/popart_canonicalization/op_builder.cc
+++ b/paddle/fluid/platform/device/ipu/popart_canonicalization/op_builder.cc
@@ -256,6 +256,69 @@ Node *CreateSoftmaxOpset11(Graph *graph,
   }
 }
 
+Node *CreateSlice(Graph *graph,
+                  Node *node,
+                  const std::vector<Node *> &inputs,
+                  const std::vector<Node *> &outputs,
+                  const std::vector<int> &starts,
+                  const std::vector<int> &ends,
+                  const std::vector<int> &axes,
+                  const std::vector<int> &strides) {
+  auto *starts_node =
+      CreateConst(
+          graph, node, starts, {int64_t(starts.size())}, ONNXDataType::INT32)
+          ->outputs[0];
+  auto *ends_node =
+      CreateConst(
+          graph, node, ends, {int64_t(ends.size())}, ONNXDataType::INT32)
+          ->outputs[0];
+  auto *axes_node =
+      CreateConst(
+          graph, node, axes, {int64_t(axes.size())}, ONNXDataType::INT32)
+          ->outputs[0];
+  auto *strides_node =
+      CreateConst(
+          graph, node, strides, {int64_t(strides.size())}, ONNXDataType::INT32)
+          ->outputs[0];
+  return CreateBaseOp(
+      graph,
+      node,
+      "popart_slice",
+      {inputs[0], starts_node, ends_node, axes_node, strides_node},
+      outputs);
+}
+
+Node *CreateSplit(Graph *graph,
+                  Node *node,
+                  const std::vector<Node *> &inputs,
+                  const std::vector<Node *> &outputs,
+                  const std::vector<int64_t> &split,
+                  const int64_t axis) {
+  if (!outputs.empty()) {
+    return CreateBaseOp(graph,
+                        node,
+                        "popart_split",
+                        inputs,
+                        outputs,
+                        {{"num_outputs", int64_t(split.size())},
+                         {"axis", int64_t(axis)},
+                         {"split", split}});
+  } else {
+    std::vector<Node *> splits_output_nodes;
+    for (int j = 0; j < split.size(); j++) {
+      splits_output_nodes.push_back(MakeVarNode(graph, node));
+    }
+    return CreateBaseOp(graph,
+                        node,
+                        "popart_split",
+                        inputs,
+                        {splits_output_nodes},
+                        {{"num_outputs", int64_t(split.size())},
+                         {"axis", int64_t(axis)},
+                         {"split", split}});
+  }
+}
+
 }  // namespace ipu
 }  // namespace platform
 }  // namespace paddle
diff --git a/paddle/fluid/platform/device/ipu/popart_canonicalization/op_builder.h b/paddle/fluid/platform/device/ipu/popart_canonicalization/op_builder.h
index 3071c2a0b90cf0756956b72d2bb6463e39139cfb..ad2a20ae324aeeed2076c9cb6589067fac37f14d 100644
--- a/paddle/fluid/platform/device/ipu/popart_canonicalization/op_builder.h
+++ b/paddle/fluid/platform/device/ipu/popart_canonicalization/op_builder.h
@@ -104,6 +104,22 @@ Node *CreateSoftmaxOpset11(Graph *graph,
                            const std::vector<Node *> &outputs,
                            int64_t axis);
 
+Node *CreateSlice(Graph *graph,
+                  Node *node,
+                  const std::vector<Node *> &inputs,
+                  const std::vector<Node *> &outputs,
+                  const std::vector<int> &starts,
+                  const std::vector<int> &ends,
+                  const std::vector<int> &axes,
+                  const std::vector<int> &strides);
+
+Node *CreateSplit(Graph *graph,
+                  Node *node,
+                  const std::vector<Node *> &inputs,
+                  const std::vector<Node *> &outputs,
+                  const std::vector<int64_t> &split,
+                  const int64_t axis);
+
 }  // namespace ipu
 }  // namespace platform
 }  // namespace paddle
diff --git a/paddle/fluid/platform/device/ipu/popart_canonicalization/tensor_ops.cc b/paddle/fluid/platform/device/ipu/popart_canonicalization/tensor_ops.cc
index 95d273bb66f01c3a3b2627903d42e3dacb069a74..9df51d5c42fc94b81372deb0afce8bd1c625d71d 100644
--- a/paddle/fluid/platform/device/ipu/popart_canonicalization/tensor_ops.cc
+++ b/paddle/fluid/platform/device/ipu/popart_canonicalization/tensor_ops.cc
@@ -249,94 +249,57 @@ Node *lookup_table_op_handler(Graph *graph,
                                     {{"value", const_value_},
                                      {"dims", const_shape_},
                                      {"dtype", GetOutputVarDType(node)}});
-    auto axes = CreateConst(graph,
-                            node,
-                            {},
-                            {},
-                            {{"value", std::vector<int64_t>{0}},
-                             {"dims", std::vector<int64_t>{1}},
-                             {"dtype", ONNXDataType::INT64}});
-    auto step = CreateConst(graph,
-                            node,
-                            {},
-                            {},
-                            {{"value", std::vector<int64_t>{1}},
-                             {"dims", std::vector<int64_t>{1}},
-                             {"dtype", ONNXDataType::INT64}});
-
-    auto left_start = CreateConst(graph,
-                                  node,
-                                  {},
-                                  {},
-                                  {{"value", std::vector<int64_t>{0}},
-                                   {"dims", std::vector<int64_t>{1}},
-                                   {"dtype", ONNXDataType::INT64}});
-    auto left_end = CreateConst(graph,
-                                node,
-                                {},
-                                {},
-                                {{"value", std::vector<int64_t>{padding_idx_}},
-                                 {"dims", std::vector<int64_t>{1}},
-                                 {"dtype", ONNXDataType::INT64}});
-
-    auto right_start =
-        CreateConst(graph,
-                    node,
-                    {},
-                    {},
-                    {{"value", std::vector<int64_t>{padding_idx_ + 1}},
-                     {"dims", std::vector<int64_t>{1}},
-                     {"dtype", ONNXDataType::INT64}});
-    auto right_end = CreateConst(graph,
-                                 node,
-                                 {},
-                                 {},
-                                 {{"value", std::vector<int64_t>{table_size_}},
-                                  {"dims", std::vector<int64_t>{1}},
-                                  {"dtype", ONNXDataType::INT64}});
-
-    auto left_slice = CreateBaseOp(graph,
-                                   node,
-                                   "popart_slice",
-                                   {GetInputVarNode("W", node),
-                                    left_start->outputs[0],
-                                    left_end->outputs[0],
-                                    axes->outputs[0],
-                                    step->outputs[0]},
-                                   {},
-                                   {});
-    auto right_slice = CreateBaseOp(graph,
-                                    node,
-                                    "popart_slice",
-                                    {GetInputVarNode("W", node),
-                                     right_start->outputs[0],
-                                     right_end->outputs[0],
-                                     axes->outputs[0],
-                                     step->outputs[0]},
-                                    {},
-                                    {});
-
     if (padding_idx_ == 0) {
+      auto right_slice =
+          CreateSlice(graph,
+                      node,
+                      {GetInputVarNode("W", node)},
+                      {},
+                      std::vector<int>{static_cast<int>(padding_idx_) + 1},
+                      std::vector<int>{static_cast<int>(table_size_)},
+                      std::vector<int>{0},
+                      std::vector<int>{1});
       w_node = CreateBaseOp(graph,
                             node,
                             "popart_concat",
                             {concat_const->outputs[0], right_slice->outputs[0]},
                             {},
                             {{"axis", int64_t(0)}});
-      ClearNode(left_start);
-      ClearNode(left_end);
-      ClearNode(left_slice);
     } else if (padding_idx_ == table_size_ - 1) {
+      auto left_slice =
+          CreateSlice(graph,
+                      node,
+                      {GetInputVarNode("W", node)},
+                      {},
+                      std::vector<int>{0},
+                      std::vector<int>{static_cast<int>(padding_idx_)},
+                      std::vector<int>{0},
+                      std::vector<int>{1});
       w_node = CreateBaseOp(graph,
                             node,
                             "popart_concat",
                             {left_slice->outputs[0], concat_const->outputs[0]},
                             {},
                             {{"axis", int64_t{0}}});
-      ClearNode(right_start);
-      ClearNode(right_end);
-      ClearNode(right_slice);
     } else {
+      auto left_slice =
+          CreateSlice(graph,
+                      node,
+                      {GetInputVarNode("W", node)},
+                      {},
+                      std::vector<int>{0},
+                      std::vector<int>{static_cast<int>(padding_idx_)},
+                      std::vector<int>{0},
+                      std::vector<int>{1});
+      auto right_slice =
+          CreateSlice(graph,
+                      node,
+                      {GetInputVarNode("W", node)},
+                      {},
+                      std::vector<int>{static_cast<int>(padding_idx_) + 1},
+                      std::vector<int>{static_cast<int>(table_size_)},
+                      std::vector<int>{0},
+                      std::vector<int>{1});
       w_node = CreateBaseOp(graph,
                             node,
                             "popart_concat",
@@ -441,72 +404,69 @@ Node *shape_handler(Graph *graph, Node *node) {
 
 Node *slice_handler(Graph *graph, Node *node) {
   auto *op = node->Op();
-  Node *starts = nullptr;
-  if (!op->HasAttr("starts")) {
-    starts = GetInputVarNode("StartsTensor", node);
-  } else {
-    auto starts_ = PADDLE_GET_CONST(std::vector<int>, op->GetAttr("starts"));
-    auto dim = int64_t(starts_.size());
-    starts = CreateConst(
-        graph, node, std::vector<int>{starts_}, {dim}, ONNXDataType::INT32);
-    starts = starts->outputs[0];
-  }
-  Node *ends = nullptr;
-  if (!op->HasAttr("ends")) {
-    ends = GetInputVarNode("EndsTensor", node);
-  } else {
-    auto ends_ = PADDLE_GET_CONST(std::vector<int>, op->GetAttr("ends"));
-    auto dim = int64_t(ends_.size());
-    ends = CreateConst(
-        graph, node, std::vector<int>{ends_}, {dim}, ONNXDataType::INT32);
-    ends = ends->outputs[0];
-  }
-  Node *axes = nullptr;
-  {
-    auto axes_ = PADDLE_GET_CONST(std::vector<int>, op->GetAttr("axes"));
-    auto dim = int64_t(axes_.size());
-    axes = CreateConst(
-        graph, node, std::vector<int>{axes_}, {dim}, ONNXDataType::INT32);
+  auto inputs = op->Inputs();
+
+  auto axes_value = PADDLE_GET_CONST(std::vector<int>, op->GetAttr("axes"));
+
+  std::vector<std::vector<int>> slice_values(3);
+  std::vector<std::string> tensor_names{"Starts", "Ends", "Strides"};
+  std::vector<std::string> attr_names{"starts", "ends", "strides"};
+  for (int i = 0; i < 3; i++) {
+    // Starts and Ends are default keys in inputs, but Strides.
+    bool is_tensor =
+        (inputs.find(tensor_names[i] + "TensorList") != inputs.end() &&
+         !inputs.at(tensor_names[i] + "TensorList").empty()) ||
+        (inputs.find(tensor_names[i] + "Tensor") != inputs.end() &&
+         !inputs.at(tensor_names[i] + "Tensor").empty());
+    if (is_tensor) {
+      PADDLE_THROW(platform::errors::Unimplemented(
+          "Do not support starts, ends and strides as tensors."));
+    } else {
+      if (i == 2 && !op->HasAttr("strides")) {
+        slice_values[i] = std::vector<int>(axes_value.size(), 1);
+      } else {
+        slice_values[i] =
+            PADDLE_GET_CONST(std::vector<int>, op->GetAttr(attr_names[i]));
+      }
+    }
   }
 
   auto decrease_axis_ =
       PADDLE_GET_CONST(std::vector<int>, op->GetAttr("decrease_axis"));
-  auto input_shape_ = GetInputVarNode("Input", node)->Var()->GetShape();
-  auto output_shape_ = GetOutputVarNode("Out", node)->Var()->GetShape();
   if (decrease_axis_.size() == 0) {
-    return CreateBaseOp(
-        graph,
-        node,
-        "popart_slice",
-        {GetInputVarNode("Input", node), starts, ends, axes->outputs[0]},
-        node->outputs);
-  } else if (output_shape_ == std::vector<int64_t>{0} ||
-             input_shape_.size() > output_shape_.size()) {
-    auto slice = CreateBaseOp(
-        graph,
-        node,
-        "popart_slice",
-        {GetInputVarNode("Input", node), starts, ends, axes->outputs[0]},
-        {},
-        {});
+    return CreateSlice(graph,
+                       node,
+                       {GetInputVarNode("Input", node)},
+                       {GetOutputVarNode("Out", node)},
+                       slice_values[0],
+                       slice_values[1],
+                       axes_value,
+                       slice_values[2]);
+  } else {
+    auto *slice = CreateSlice(graph,
+                              node,
+                              {GetInputVarNode("Input", node)},
+                              {},
+                              slice_values[0],
+                              slice_values[1],
+                              axes_value,
+                              slice_values[2])
+                      ->outputs[0];
     return CreateBaseOp(
         graph,
         node,
         "popart_squeeze",
-        {slice->outputs[0]},
+        {slice},
         {GetOutputVarNode("Out", node)},
         {{"axes",
           std::vector<int64_t>{decrease_axis_.begin(), decrease_axis_.end()}}});
-  } else {
-    return CreateBaseOp(
-        graph,
-        node,
-        "popart_slice",
-        {GetInputVarNode("Input", node), starts, ends, axes->outputs[0]},
-        node->outputs);
   }
 }
 
+Node *strided_slice_handler(Graph *graph, Node *node) {
+  return slice_handler(graph, node);
+}
+
 Node *expand_handler(Graph *graph, Node *node) {
   auto *op = node->Op();
   if (!op->Input("expand_times_tensor").empty()) {
@@ -552,6 +512,10 @@ Node *assign_handler(Graph *graph, Node *node) {
                       {});
 }
 
+Node *share_data_handler(Graph *graph, Node *node) {
+  return assign_handler(graph, node);
+}
+
 Node *assign_value_handler(Graph *graph, Node *node) {
   auto *op = node->Op();
   auto dtype_ = PADDLE_GET_CONST(int, op->GetAttr("dtype"));
@@ -731,15 +695,12 @@ Node *split_handler(Graph *graph, Node *node) {
   auto *op = node->Op();
   auto axis = PADDLE_GET_CONST(int, op->GetAttr("axis"));
   auto sections = PADDLE_GET_CONST(std::vector<int>, op->GetAttr("sections"));
-  return CreateBaseOp(
-      graph,
-      node,
-      "popart_split",
-      {GetInputVarNode("X", node)},
-      node->outputs,
-      {{"num_outputs", int64_t(sections.size())},
-       {"axis", int64_t(axis)},
-       {"split", std::vector<int64_t>{sections.begin(), sections.end()}}});
+  return CreateSplit(graph,
+                     node,
+                     {GetInputVarNode("X", node)},
+                     node->outputs,
+                     std::vector<int64_t>{sections.begin(), sections.end()},
+                     axis);
 }
 
 Node *dot_handler(Graph *graph, Node *node) {
@@ -1116,19 +1077,8 @@ Node *flip_handler(Graph *graph, Node *node) {
     auto axis = axes[i];
     std::vector<int64_t> split;
     split.resize(input_shape[axis], 1);
-    std::vector<Node *> splits_output_nodes;
-    for (int j = 0; j < split.size(); j++) {
-      splits_output_nodes.push_back(MakeVarNode(graph, node));
-    }
-    auto splits_outputs = CreateBaseOp(graph,
-                                       node,
-                                       "popart_split",
-                                       {temp_node},
-                                       {splits_output_nodes},
-                                       {{"num_outputs", int64_t(split.size())},
-                                        {"axis", int64_t(axis)},
-                                        {"split", split}})
-                              ->outputs;
+    auto splits_outputs =
+        CreateSplit(graph, node, {temp_node}, {}, split, axis)->outputs;
     std::reverse(splits_outputs.begin(), splits_outputs.end());
     if (i != axes.size() - 1) {
       temp_node = CreateBaseOp(graph,
@@ -1244,6 +1194,70 @@ Node *p_norm_handler(Graph *graph, Node *node) {
                       {GetOutputVarNode("Out", node)});
 }
 
+Node *tile_handler(Graph *graph, Node *node) {
+  auto *op = node->Op();
+  auto inputs = op->Inputs();
+  bool is_repeat_tensors =
+      (inputs.find("RepeatTimes") != inputs.end() &&
+       !inputs.at("RepeatTimes").empty()) ||
+      (inputs.find("repeat_times_tensor") != inputs.end() &&
+       !inputs.at("repeat_times_tensor").empty());
+  if (is_repeat_tensors) {
+    PADDLE_THROW(
+        platform::errors::Unimplemented("Do not support repeats as tensors."));
+  }
+  auto repeat_times =
+      PADDLE_GET_CONST(std::vector<int>, op->GetAttr("repeat_times"));
+  int nums = repeat_times.size();
+  std::vector<int> ones(
+      GetInputVarNode("X", node)->Var()->GetShape().size() - nums, 1);
+  repeat_times.insert(repeat_times.begin(), ones.begin(), ones.end());
+  auto *repeat_node = CreateConst(graph,
+                                  node,
+                                  std::vector<int64_t>{repeat_times.begin(),
+                                                       repeat_times.end()},
+                                  {int64_t(repeat_times.size())},
+                                  ONNXDataType::INT64)
+                          ->outputs[0];
+  return CreateBaseOp(graph,
+                      node,
+                      "popart_tile",
+                      {GetInputVarNode("X", node), repeat_node},
+                      {GetOutputVarNode("Out", node)});
+}
+
+Node *unstack_handler(Graph *graph, Node *node) {
+  auto *op = node->Op();
+  auto axis = PADDLE_GET_CONST(int, op->GetAttr("axis"));
+  if (axis < 0) {
+    axis += GetInputVarNode("X", node)->Var()->GetShape().size();
+  }
+  std::vector<int64_t> split(node->outputs.size(), 1);
+  auto split_output_nodes =
+      CreateSplit(graph, node, {GetInputVarNode("X", node)}, {}, split, axis)
+          ->outputs;
+  Node *output = nullptr;
+  for (int i = 0; i < split.size(); i++) {
+    output = CreateBaseOp(graph,
+                          node,
+                          "popart_squeeze",
+                          {split_output_nodes[i]},
+                          {node->outputs[i]},
+                          {{"axes", std::vector<int64_t>{axis}}});
+  }
+  return output;
+}
+
+Node *where_handler(Graph *graph, Node *node) {
+  return CreateBaseOp(graph,
+                      node,
+                      "popart_where",
+                      {GetInputVarNode("Condition", node),
+                       GetInputVarNode("X", node),
+                       GetInputVarNode("Y", node)},
+                      {GetOutputVarNode("Out", node)});
+}
+
 }  // namespace
 }  // namespace ipu
 }  // namespace platform
@@ -1265,6 +1279,7 @@ REGISTER_HANDLER(concat, concat_handler);
 REGISTER_HANDLER(stack, stack_handler);
 REGISTER_HANDLER(shape, shape_handler);
 REGISTER_HANDLER(slice, slice_handler);
+REGISTER_HANDLER(strided_slice, strided_slice_handler);
 REGISTER_HANDLER(expand, expand_handler);
 REGISTER_HANDLER(expand_v2, expand_v2_handler);
 REGISTER_HANDLER(expand_as_v2, expand_as_v2_handler);
@@ -1281,3 +1296,7 @@ REGISTER_HANDLER(dist, dist_handler);
 REGISTER_HANDLER(flip, flip_handler);
 REGISTER_HANDLER(meshgrid, meshgrid_handler);
 REGISTER_HANDLER(p_norm, p_norm_handler);
+REGISTER_HANDLER(share_data, share_data_handler);
+REGISTER_HANDLER(tile, tile_handler);
+REGISTER_HANDLER(unstack, unstack_handler);
+REGISTER_HANDLER(where, where_handler);