!3435 Add parallel operator for Tile

Merge pull request !3435 from yangzhenzhang/add-tile-op

mindspore/ccsrc/frontend/parallel/auto_parallel/operator_costmodel.cc

@@ -198,8 +198,8 @@ double ActivationCost::GetBackwardCommCost(const std::vector<TensorInfo> &inputs
 // this operator uses
 double ActivationCost::GetForwardComputationCost(const std::vector<TensorInfo> &inputs, const std::vector<TensorInfo> &,
                                                  int32_t) const {
-  TensorInfo input0_info = inputs[0];
-  Shape input0_slice_shape = input0_info.slice_shape();
+  TensorInfo input0 = inputs[0];
+  Shape input0_slice_shape = input0.slice_shape();
   return ListProduct(input0_slice_shape) * static_cast<double>(inputs_type_lengths_[0]);
 }
 
@@ -240,12 +240,16 @@ double SoftmaxCost::GetBackwardCommCost(const std::vector<TensorInfo> &inputs, c
 
 // Return the per device computation cost in the forward phase. The cost is calculated according to the bytes
 // this operator uses
-double SoftmaxCost::GetForwardComputationCost(const std::vector<TensorInfo> &inputs, const std::vector<TensorInfo> &,
+double SoftmaxCost::GetForwardComputationCost(const std::vector<TensorInfo> &, const std::vector<TensorInfo> &outputs,
                                               int32_t) const {
-  // In the forward phase, the computation cost = slice(A)
-  TensorInfo input0 = inputs[0];
-  Shape input0_slice_shape = input0.slice_shape();
-  return ListProduct(input0_slice_shape) * static_cast<double>(inputs_type_lengths_[0]);
+  if (outputs.empty() || outputs_type_lengths_.empty()) {
+    MS_LOG(EXCEPTION) << "The outputs or outputs_type_length is empty";
+  }
+
+  // use output for Tile operator
+  TensorInfo output_info = outputs[0];
+  Shape output_slice_shape = output_info.slice_shape();
+  return ListProduct(output_slice_shape) * static_cast<double>(outputs_type_lengths_[0]);
 }
 
 // Return the per device computation cost in the forward phase. The cost is calculated according to the bytes

mindspore/ccsrc/frontend/parallel/auto_parallel/operator_costmodel.h

@@ -195,6 +195,8 @@ class SoftmaxCost : public OperatorCost {
                                     int32_t) const override;
 };
 using SoftmaxCostPtr = std::shared_ptr<SoftmaxCost>;
+using TileCost = SoftmaxCost;
+using TileCostPtr = std::shared_ptr<TileCost>;
 
 class TmpIdentityCost : public OperatorCost {
  public:

mindspore/ccsrc/frontend/parallel/dynamic_creator.h

@@ -133,6 +133,7 @@ REGISTER(SigmoidCrossEntropyWithLogitsInfo);
 REGISTER(SquareInfo);
 REGISTER(GatherV2PInfo);
 REGISTER(EmbeddingLookupInfo);
+REGISTER(TileInfo);
 }  // namespace parallel
 }  // namespace mindspore
 

mindspore/ccsrc/frontend/parallel/ops_info/ops_info_head_files.h

@@ -37,5 +37,6 @@
 #include "frontend/parallel/ops_info/transpose_info.h"
 #include "frontend/parallel/ops_info/virtual_dataset_info.h"
 #include "frontend/parallel/ops_info/gather_v2_p_info.h"
+#include "frontend/parallel/ops_info/tile_info.h"
 
 #endif  // MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_HEAD_FILES_H_

mindspore/ccsrc/frontend/parallel/ops_info/ops_utils.h

@@ -182,6 +182,7 @@ constexpr char RELU[] = "ReLU";
 constexpr char ONEHOT[] = "OneHot";
 constexpr char DROPOUT_DO_MASK[] = "DropoutDoMask";
 constexpr char DROPOUT_GEN_MASK[] = "DropoutGenMask";
+constexpr char TILE[] = "Tile";
 constexpr char REDUCE_MAX[] = "ReduceMax";
 constexpr char REDUCE_MIN[] = "ReduceMin";
 constexpr char REDUCE_SUM[] = "ReduceSum";

mindspore/ccsrc/frontend/parallel/ops_info/tile_info.cc

+/**
+ * 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 "frontend/parallel/ops_info/tile_info.h"
+
+#include <algorithm>
+#include <memory>
+#include <utility>
+#include <vector>
+
+#include "frontend/parallel/device_matrix.h"
+#include "frontend/parallel/strategy.h"
+#include "frontend/parallel/tensor_layout/tensor_redistribution.h"
+#include "pipeline/jit/resource.h"
+
+namespace mindspore {
+namespace parallel {
+// get the multiples
+Status TileInfo::GetAttrs() {
+  if (input_value_.size() < 2) {
+    MS_LOG(ERROR) << name_ << ": The size of input value is smaller than 2.";
+    return FAILED;
+  }
+  if (input_value_[1] == nullptr) {
+    MS_LOG(ERROR) << name_ << ": The multiples is null.";
+    return FAILED;
+  }
+
+  std::vector<ValuePtr> elements;
+  ValueTuplePtr multiples = input_value_[1]->cast<ValueTuplePtr>();
+  if (multiples == nullptr) {
+    MS_LOG(ERROR) << name_ << ": Input_value_[1] must be ValueTuplePtr.";
+    return FAILED;
+  }
+  elements = multiples->value();
+  if (elements.size() != outputs_shape_[0].size()) {
+    MS_LOG(ERROR) << name_ << ": Elements size must equal to outputs shape[0] size.";
+    return FAILED;
+  }
+
+  for (auto &element : elements) {
+    MS_EXCEPTION_IF_NULL(element);
+    if (element->isa<Int32Imm>()) {
+      int32_t axis = element->cast<Int32ImmPtr>()->value();
+      full_multiples_.push_back(axis);
+    } else {
+      MS_LOG(ERROR) << name_ << ": The value of axis must be int32.";
+      return FAILED;
+    }
+  }
+
+  return SUCCESS;
+}
+
+Status TileInfo::CheckStrategy(const StrategyPtr &strategy) {
+  Shapes multiples = {full_multiples_};
+  if (CheckStrategyValue(strategy, multiples, is_auto_parallel_) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Invalid strategy.";
+    return FAILED;
+  }
+
+  return SUCCESS;
+}
+
+Status TileInfo::InferDevMatrixShape() {
+  MS_EXCEPTION_IF_NULL(strategy_);
+  std::vector<Dimensions> stra = strategy_->GetInputDim();
+  if (stra.empty()) {
+    MS_LOG(ERROR) << name_ << "The strategy is empty";
+    return FAILED;
+  }
+  if (full_multiples_.size() != stra[0].size()) {
+    MS_LOG(ERROR) << name_ << ": Invalid strategy";
+    return FAILED;
+  }
+
+  dev_matrix_shape_ = stra[0];
+
+  slice_multiples_ = full_multiples_;
+  for (size_t i = 0; i < full_multiples_.size(); ++i) {
+    slice_multiples_[i] = slice_multiples_[i] / dev_matrix_shape_[i];
+  }
+  return SUCCESS;
+}
+
+Status TileInfo::InferTensorMap() {
+  TensorMap input_tensor_map;
+  TensorMap output_tensor_map;
+  if (inputs_shape_.empty() || outputs_shape_.empty()) {
+    MS_LOG(ERROR) << name_ << "The inputs or outputs' shape is empty";
+    return FAILED;
+  }
+
+  // the input tensor cannot be split
+  for (size_t i = 0; i < inputs_shape_[0].size(); ++i) {
+    input_tensor_map.push_back(MAP_NONE);
+  }
+
+  // cannot use dev_matrix_shape_ replace outputs_shape_[0], because it may not be fully split in all devices.
+  int32_t size = SizeToInt(outputs_shape_[0].size());
+  for (int i = 0; i < size; ++i) {
+    output_tensor_map.push_back(size - i - 1);
+  }
+
+  inputs_tensor_map_.push_back(input_tensor_map);
+  outputs_tensor_map_.push_back(output_tensor_map);
+  return SUCCESS;
+}
+
+Status TileInfo::InferMirrorOps() {
+  mirror_ops_.clear();
+  Shape input_tensor_map = inputs_tensor_map_[0];
+  std::vector<Group> group;
+  if (CreateGroupByTensorMap(input_tensor_map, &group) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Create group for input failed.";
+    return FAILED;
+  }
+
+  if (group.empty()) {
+    MS_LOG(INFO) << name_ << ": The mirror group is empty.";
+    return SUCCESS;
+  }
+
+  OperatorVector input_op, multiples_op;
+  input_op = CreateMirrorOps(group[0].name(), group[0].GetDevNum());
+  mirror_ops_.push_back(input_op);
+  mirror_ops_.push_back(multiples_op);
+  return SUCCESS;
+}
+
+Status TileInfo::InferTensorInfo() {
+  if (inputs_shape_.empty() || outputs_shape_.empty() || inputs_tensor_map_.empty() || outputs_tensor_map_.empty()) {
+    MS_LOG(ERROR) << name_ << ": Invalid args";
+    return FAILED;
+  }
+  // infer tensor layout
+  TensorLayout input_layout, output_layout;
+  if (input_layout.InitFromVector(dev_matrix_shape_, inputs_tensor_map_[0], inputs_shape_[0]) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Infer input tensor layout failed.";
+    return FAILED;
+  }
+  if (output_layout.InitFromVector(dev_matrix_shape_, outputs_tensor_map_[0], outputs_shape_[0]) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Infer output tensor layout failed.";
+    return FAILED;
+  }
+
+  TensorInfo input_tensor_info(input_layout);
+  TensorInfo output_tensor_info(output_layout);
+
+  inputs_tensor_info_.push_back(input_tensor_info);
+  outputs_tensor_info_.push_back(output_tensor_info);
+  return SUCCESS;
+}
+
+void TileInfo::UpdateMultiples(const CNodePtr &cnode) {
+  MS_EXCEPTION_IF_NULL(cnode);
+  if (cnode->size() != 3) {
+    MS_LOG(EXCEPTION) << "The size of tile cnode's inputs must be 3";
+  }
+
+  if (!IsValueNode<ValueTuple>(cnode->input(2))) {
+    MS_LOG(EXCEPTION) << "The input[2] of tile cnode is not ValueTuple.";
+  }
+
+  auto func_graph = cnode->func_graph();
+  MS_EXCEPTION_IF_NULL(func_graph);
+  auto manager = func_graph->manager();
+  MS_EXCEPTION_IF_NULL(manager);
+
+  ValuePtr new_multiples = MakeValue(slice_multiples_);
+  AnfNodePtr val = NewValueNode(new_multiples);
+  (void)manager->Replace(cnode->input(2), val);
+}
+
+std::shared_ptr<std::vector<std::vector<int32_t>>> TileInfo::GenerateBatchStrategies() {
+  if (InferAttrs() != SUCCESS) {
+    MS_LOG(EXCEPTION) << name_ << ": Infer attrs failed";
+  }
+  Shapes multiples_shape = {full_multiples_};
+  split_flag_list_ = {true};
+  return GenerateBatchStrategiesBySplitFlag(multiples_shape, split_flag_list_);
+}
+
+Status TileInfo::SetCostUnderStrategy(const StrategyPtr &strategy) {
+  if (SetCostUnderStrategyBase(strategy) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Set cost under strategy failed.";
+    return FAILED;
+  }
+
+  return SUCCESS;
+}
+
+Status TileInfo::GenerateStrategies(int32_t stage_id) {
+  if (InferAttrs() != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Infer attrs failed";
+    return FAILED;
+  }
+  Shape multiples_split(full_multiples_.size(), 1);
+  Shapes splittable_inputs = {multiples_split};
+
+  std::vector<StrategyPtr> sp_vector;
+  is_auto_parallel_ = true;
+  Shapes tmp_inputs_shape = {full_multiples_};
+  if (GenerateStrategiesForIndependentInputs(stage_id, tmp_inputs_shape, splittable_inputs, &sp_vector) != SUCCESS) {
+    return FAILED;
+  }
+
+  size_t success = 0;
+  for (auto &sp : sp_vector) {
+    PrintStrategy(sp);
+    if (SetCostUnderStrategy(sp) == SUCCESS) {
+      success++;
+      MS_LOG(INFO) << name_ << ": Successfully generated " << success << " strategy.";
+      PrintStrategy(sp);
+    }
+  }
+  return SUCCESS;
+}
+
+Status TileInfo::Init(const StrategyPtr &strategy) {
+  if (InitWithAutoRepeatCalc(strategy) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Init failed.";
+    return FAILED;
+  }
+  MS_LOG(INFO) << name_ << ": Init success.";
+  return SUCCESS;
+}
+
+Status TileInfo::InitForCostModel(const StrategyPtr &strategy) {
+  if (InitForCostModelWithAutoRepeatCalc(strategy) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Init for cost model failed.";
+    return FAILED;
+  }
+
+  MS_LOG(INFO) << name_ << ": Init for cost model success.";
+  return SUCCESS;
+}
+}  // namespace parallel
+}  // namespace mindspore

mindspore/ccsrc/frontend/parallel/ops_info/tile_info.h

+/**
+ * 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_FRONTEND_PARALLEL_OPS_INFO_TILE_INFO_H_
+#define MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_TILE_INFO_H_
+
+#include <string>
+
+#include <memory>
+#include <unordered_map>
+#include <vector>
+
+#include "ir/value.h"
+#include "frontend/parallel/auto_parallel/operator_costmodel.h"
+#include "frontend/parallel/ops_info/operator_info.h"
+#include "frontend/parallel/strategy.h"
+
+namespace mindspore {
+namespace parallel {
+class TileInfo : public OperatorInfo {
+ public:
+  TileInfo(const std::string &operator_name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+           const PrimitiveAttrs &attrs)
+      : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs, std::make_shared<TileCost>(false)) {}
+  ~TileInfo() override = default;
+
+  Status Init(const StrategyPtr &strategy) override;
+  Status InitForCostModel(const StrategyPtr &strategy) override;
+  Status GenerateStrategies(int32_t) override;
+  Status SetCostUnderStrategy(const StrategyPtr &) override;
+  std::shared_ptr<std::vector<std::vector<int32_t>>> GenerateBatchStrategies() override;
+  void UpdateMultiples(const CNodePtr &cnode);
+
+ protected:
+  Status GetAttrs() override;
+  Status CheckStrategy(const StrategyPtr &strategy) override;
+  Status InferMirrorOps() override;
+  Status InferForwardCommunication() override { return SUCCESS; }
+  Status InferTensorInfo() override;
+  Status InferDevMatrixShape() override;
+  Status InferTensorMap() override;
+
+ private:
+  std::vector<int32_t> full_multiples_;
+  std::vector<int32_t> slice_multiples_;
+};
+
+using TileInfoPtr = std::shared_ptr<TileInfo>;
+}  // namespace parallel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_TILE_INFO_H_

mindspore/ccsrc/frontend/parallel/step_auto_parallel.cc

@@ -260,7 +260,7 @@ bool IsSplittableOperator(const std::string &op_name) {
      REDUCE_MAX, REDUCE_MIN, ARGMAXWITHVALUE, ARGMINWITHVALUE, REDUCE_SUM, CONV2D, FUSE_BATCH_NORM, POOLING,
      MAX_POOL_WITH_ARGMAX, SIMPLE_MEAN, FLATTEN, BATCH_NORM, LAYER_NORM, BIAS_ADD, ASSIGN_SUB, COS, ACOS, EXP,
      LOG, REDUCE_MEAN, REAL_DIV, SIGMOID, POW, MAXIMUM, MINIMUM, EQUAL, NOT_EQUAL, LOGICALNOT, GATHERV2, SQRT,
-     STRIDEDSLICE, GET_NEXT, CAST, NEG, SQUARE, BATCH_MATMUL, EXPAND_DIMS, SQUEEZE, SPARSE_GATHERV2,
+     STRIDEDSLICE, GET_NEXT, CAST, NEG, SQUARE, BATCH_MATMUL, EXPAND_DIMS, SQUEEZE, SPARSE_GATHERV2, TILE,
      SOFTMAX_CROSS_ENTROPY_WITH_LOGITS, SIGMOID_CROSS_ENTROPY_WITH_LOGITS, SPARSE_SOFTMAX_CROSS_ENTROPY_WITH_LOGITS};
   // clang-format on
 

mindspore/ccsrc/frontend/parallel/step_parallel.cc

@@ -1890,8 +1890,24 @@ void HandleDropoutNode(const OperatorInfoPtr &distribute_operator, const CNodePt
   ReplaceOneOp(replace_op[0], cnode->input(DROPOUT_GEN_MASK_INDEX)->cast<CNodePtr>());
 }
 
+void HandleTileNode(const OperatorInfoPtr &distribute_operator, const CNodePtr &cnode) {
+  MS_EXCEPTION_IF_NULL(cnode);
+  if (cnode->size() < 3 || !IsValueNode<Primitive>(cnode->input(0))) {
+    return;
+  }
+  auto prim = GetValueNode<PrimitivePtr>(cnode->input(0));
+  if (prim->name() != TILE) {
+    return;
+  }
+
+  TileInfoPtr tile = std::dynamic_pointer_cast<TileInfo>(distribute_operator);
+  MS_EXCEPTION_IF_NULL(tile);
+  tile->UpdateMultiples(cnode);
+}
+
 void HandleSpecialNode(const OperatorInfoPtr &distribute_operator, const CNodePtr &cnode) {
   HandleDropoutNode(distribute_operator, cnode);
+  HandleTileNode(distribute_operator, cnode);
 }
 
 std::set<FuncGraphPtr> FindForwardGraphByRootNodes(const AnfNodeSet &root_all_nodes) {

tests/ut/python/parallel/test_tile.py

+# 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 mindspore as ms
+from mindspore import context, Tensor, Parameter
+from mindspore.common.api import _executor
+from mindspore.nn import Cell, TrainOneStepCell, Momentum
+from mindspore.ops import operations as P
+
+
+class Net(Cell):
+    def __init__(self, weight, weight2, strategy1=None, strategy2=None, is_parameter=True):
+        super().__init__()
+        self.mul = P.Mul().set_strategy(strategy1)
+        self.tile = P.Tile().set_strategy(strategy2)
+        if is_parameter:
+            self.weight = Parameter(weight, "w1")
+        else:
+            self.weight = weight
+        self.mul2 = P.Mul()
+        self.weight2 = Parameter(weight2, "w2")
+
+    def construct(self, x, b):
+        out = self.tile(self.weight, (8, 4, 2))
+        out = self.mul(x, out)
+        out = self.mul2(out, self.weight2)
+        return out
+
+
+class Net2(Cell):
+    def __init__(self, weight2, strategy1=None, strategy2=None):
+        super().__init__()
+        self.mul = P.Mul().set_strategy(strategy1)
+        self.tile = P.Tile().set_strategy(strategy2)
+        self.weight2 = Parameter(weight2, "w2")
+
+    def construct(self, x, b):
+        out = self.mul(x, self.weight2)
+        out = self.tile(out, (8, 8, 4, 2))
+        return out
+
+
+_x = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+_w1 = Tensor(np.ones([16, 16, 16]), dtype=ms.float32)
+_w2 = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+_b = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+
+
+def compile_net(net):
+    context.set_context(save_graphs=True)
+    optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
+    train_net = TrainOneStepCell(net, optimizer)
+    train_net.set_auto_parallel()
+    _executor.compile(train_net, _x, _b)
+    context.reset_auto_parallel_context()
+
+
+def test_tile_parameter():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((2, 2, 2), (2, 2, 2))
+    strategy2 = ((2, 2, 2),)
+    net = Net(_w1, _w2, strategy1, strategy2, is_parameter=True)
+    compile_net(net)
+
+
+def test_tile_parameter_no_full_split():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((2, 2, 2), (2, 2, 2))
+    strategy2 = ((2, 2, 1),)
+    net = Net(_w1, _w2, strategy1, strategy2, is_parameter=True)
+    compile_net(net)
+
+
+def test_tile_tensor():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((2, 2, 2), (2, 2, 2))
+    strategy2 = ((2, 2, 2),)
+    net = Net(_w1, _w2, strategy1, strategy2, is_parameter=False)
+    compile_net(net)
+
+
+def test_tile_tensor_no_full_split():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((2, 2, 2), (2, 2, 2))
+    strategy2 = ((2, 2, 1),)
+    net = Net(_w1, _w2, strategy1, strategy2, is_parameter=False)
+    compile_net(net)
+
+
+def test_tile_output():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((2, 2, 2), (2, 2, 2))
+    strategy2 = ((1, 2, 2, 2),)
+    net = Net2(_w2, strategy1, strategy2)
+    compile_net(net)
+
+def test_tile_output_no_full_split():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((2, 2, 2), (2, 2, 2))
+    strategy2 = ((1, 2, 1, 2),)
+    net = Net2(_w2, strategy1, strategy2)
+    compile_net(net)
+
+
+def test_tile_no_strategy():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((2, 2, 2), (2, 2, 2))
+    strategy2 = None
+    net = Net2(_w2, strategy1, strategy2)
+    compile_net(net)
+
+def test_tile_auto_parallel():
+    context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
+    net = Net2(_w2)
+    compile_net(net)