add fp16 tile and explain range

mindspore/lite/nnacl/base/tile_base.c

@@ -0,0 +1,60 @@
+/**
+ * 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 "nnacl/base/tile_base.h"
+#include <string.h>
+
+void DoCopyData(const uint8_t *input_data, uint8_t *output_data, size_t size, size_t data_size, size_t multiple) {
+  uint8_t *out_data = output_data;
+  for (size_t i = 0; i < multiple; ++i) {
+    (void)memcpy(out_data, input_data, size * sizeof(uint8_t) * data_size);
+    out_data += size * data_size;
+  }
+}
+
+int DoTileOneDimension(uint8_t *input_data, uint8_t *output_data, size_t dim, TileParameter *parameter) {
+  size_t src_dim_size = parameter->in_shape_[dim];
+  if (dim == parameter->in_dim_ - 1) {
+    DoCopyData(input_data, output_data, src_dim_size, parameter->data_size_, parameter->multiples_[dim]);
+    return 0;
+  }
+  for (size_t i = 0; i < src_dim_size; ++i) {
+    for (size_t j = 0; j < parameter->multiples_[dim]; ++j) {
+      size_t in_pos = parameter->in_strides_[dim] * i;
+      size_t out_pos = parameter->out_strides_[dim] * (i + j * src_dim_size);
+      DoTileOneDimension(input_data + in_pos * parameter->data_size_, output_data + out_pos * parameter->data_size_,
+                         dim + 1, parameter);
+    }
+  }
+  return 0;
+}
+
+void Tile(void *input_data, void *output_data, TileParameter *parameter) {
+  DoTileOneDimension((uint8_t *)input_data, (uint8_t *)output_data, 0, parameter);
+}
+
+void TileSimple(void *input_data, void *output_data, size_t begin, size_t end, TileParameter *parameter) {
+  uint8_t *out_data = output_data;
+  uint8_t *in_data = input_data;
+  for (size_t i = begin; i < end; ++i) {
+    uint8_t *src = in_data + i * parameter->fast_stride_ * parameter->data_size_;
+    uint8_t *dst = out_data + i * parameter->fast_stride_ * parameter->fast_multiple_ * parameter->data_size_;
+    for (size_t m = 0; m < parameter->fast_multiple_; ++m) {
+      (void)memcpy(dst, src, parameter->fast_stride_ * parameter->data_size_);
+      dst += parameter->fast_stride_ * parameter->data_size_;
+    }
+  }
+}

mindspore/lite/nnacl/fp32/tile_fp32.h → mindspore/lite/nnacl/base/tile_base.h

@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-#ifndef MINDSPORE_LITE_NNACL_TILE_H_
-#define MINDSPORE_LITE_NNACL_TILE_H_
+#ifndef MINDSPORE_LITE_NNACL_BASE_TILE_H_
+#define MINDSPORE_LITE_NNACL_BASE_TILE_H_
 
 #include "nnacl/op_base.h"
 
@@ -33,14 +33,19 @@ typedef struct TileParameter {
 
   // other parameter
   int in_dim_;
+  size_t data_size_;
+  size_t fast_outer_size_;
+  size_t fast_stride_;
+  size_t fast_multiple_;
 } TileParameter;
 
 #ifdef __cplusplus
 extern "C" {
 #endif
-void Tile(float *input_data, float *output_data, TileParameter *parameter);
+void Tile(void *input_data, void *output_data, TileParameter *parameter);
+void TileSimple(void *input_data, void *output_data, size_t begin, size_t end, TileParameter *parameter);
 #ifdef __cplusplus
 }
 #endif
 
-#endif  // MINDSPORE_LITE_NNACL_TILE_H_
+#endif  // MINDSPORE_LITE_NNACL_BASE_TILE_H_

mindspore/lite/nnacl/fp32/tile_fp32.c

@@ -1,46 +0,0 @@
-/**
- * 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 "nnacl/fp32/tile_fp32.h"
-#include <string.h>
-
-void DoCopyData(const float *input_data, float *output_data, size_t size, size_t multiple) {
-  float *out_data = output_data;
-  for (size_t i = 0; i < multiple; ++i) {
-    (void)memcpy(out_data, input_data, size * sizeof(float));
-    out_data += size;
-  }
-}
-
-int DoTileOneDimension(float *input_data, float *output_data, size_t dim, TileParameter *parameter) {
-  size_t src_dim_size = parameter->in_shape_[dim];
-  if (dim == parameter->in_dim_ - 1) {
-    DoCopyData(input_data, output_data, src_dim_size, parameter->multiples_[dim]);
-    return 0;
-  }
-  for (size_t i = 0; i < src_dim_size; ++i) {
-    for (size_t j = 0; j < parameter->multiples_[dim]; ++j) {
-      size_t in_pos = parameter->in_strides_[dim] * i;
-      size_t out_pos = parameter->out_strides_[dim] * (i + j * src_dim_size);
-      DoTileOneDimension(input_data + in_pos, output_data + out_pos, dim + 1, parameter);
-    }
-  }
-  return 0;
-}
-
-void Tile(float *input_data, float *output_data, TileParameter *parameter) {
-  DoTileOneDimension(input_data, output_data, 0, parameter);
-}

mindspore/lite/src/ops/populate/tile_populate.cc

@@ -17,7 +17,7 @@
 #include "src/ops/tile.h"
 #include "src/ops/primitive_c.h"
 #include "src/ops/populate/populate_register.h"
-#include "nnacl/fp32/tile_fp32.h"
+#include "nnacl/base/tile_base.h"
 
 namespace mindspore {
 namespace lite {

mindspore/lite/src/ops/squeeze.cc

@@ -133,7 +133,7 @@ int Squeeze::InferShape(std::vector<Tensor *> inputs_, std::vector<Tensor *> out
     }
   }
   outputs_.front()->set_shape(out_shape);
-  return 0;
+  return RET_OK;
 }
 }  // namespace lite
 }  // namespace mindspore

mindspore/lite/src/runtime/kernel/arm/base/tile_base.cc

@@ -0,0 +1,153 @@
+/**
+ * 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 "src/runtime/kernel/arm/base/tile_base.h"
+#include "src/kernel_registry.h"
+#include "include/errorcode.h"
+
+using mindspore::lite::KernelRegistrar;
+using mindspore::lite::RET_ERROR;
+using mindspore::lite::RET_OK;
+using mindspore::schema::PrimitiveType_Tile;
+
+namespace mindspore::kernel {
+namespace {
+constexpr size_t kDoubleInputsSize = 2;
+}
+int TileCPUKernel::Init() {
+  if (!InferShapeDone()) {
+    return RET_OK;
+  }
+  return ReSize();
+}
+
+void TileCPUKernel::ComputeStrides(const int *shape, int *strides, int ndim) {
+  int stride = 1;
+  for (int i = ndim - 1; i >= 0; i--) {
+    strides[i] = stride;
+    stride *= shape[i];
+  }
+}
+
+int TileCPUKernel::ReSize() {
+  auto tile_parameter_ = reinterpret_cast<TileParameter *>(op_parameter_);
+  MS_ASSERT(tile_parameter_);
+  if (in_tensors_.size() == kDoubleInputsSize) {
+    if (in_tensors_[1]->ElementsNum() > static_cast<int>(in_tensors_[0]->shape().size())) {
+      MS_LOG(ERROR) << "tile's input1 data_num cannot be larger than input0's shape_size.";
+      return false;
+    }
+    auto input1_addr = reinterpret_cast<int *>(in_tensors_[1]->data_c());
+    for (int i = 0; i < in_tensors_[1]->ElementsNum(); ++i) {
+      tile_parameter_->dims_[i] = i;
+      tile_parameter_->multiples_[i] = input1_addr[i];
+    }
+  }
+  tile_parameter_->in_dim_ = in_tensors_.at(0)->shape().size();
+  for (int i = 0; i < tile_parameter_->in_dim_; ++i) {
+    tile_parameter_->in_shape_[i] = in_tensors_.at(0)->shape().at(i);
+    tile_parameter_->out_shape_[i] = out_tensors_.at(0)->shape().at(i);
+  }
+  ComputeStrides(tile_parameter_->in_shape_, tile_parameter_->in_strides_, tile_parameter_->in_dim_);
+  ComputeStrides(tile_parameter_->out_shape_, tile_parameter_->out_strides_, tile_parameter_->in_dim_);
+
+  auto data_type = in_tensors_.at(0)->data_type();
+  if (data_type == kNumberTypeFloat32 || data_type == kNumberTypeInt32) {
+    tile_parameter_->data_size_ = sizeof(float);
+  } else if (data_type == kNumberTypeFloat16) {
+    tile_parameter_->data_size_ = sizeof(float) / 2;
+  } else {
+    MS_LOG(ERROR) << "tile not support data type: " << data_type;
+    return RET_ERROR;
+  }
+
+  FillOneDimTileParam();
+  return RET_OK;
+}
+
+int SimpleTile(void *cdata, int task_id) {
+  auto kernel = reinterpret_cast<TileCPUKernel *>(cdata);
+  auto ret = kernel->SimpleTileImpl(task_id);
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "SimpleTile error task_id[" << task_id << "] error_code[" << ret << "]";
+    return ret;
+  }
+  return RET_OK;
+}
+
+void TileCPUKernel::FillOneDimTileParam() {
+  // check if tile exact one dim
+  auto tile_parameter_ = reinterpret_cast<TileParameter *>(op_parameter_);
+  MS_ASSERT(tile_parameter_);
+  int large_one_multiple_count = 0;
+  int multiple;
+  int mul_index;
+  for (auto i = 0; i < tile_parameter_->in_dim_; ++i) {
+    if (tile_parameter_->multiples_[i] > 1) {
+      large_one_multiple_count++;
+      multiple = tile_parameter_->multiples_[i];
+      mul_index = i;
+    }
+  }
+  one_dim_tile_ = large_one_multiple_count == 1;
+  if (one_dim_tile_) {
+    tile_parameter_->fast_multiple_ = static_cast<size_t>(multiple);
+    tile_parameter_->fast_stride_ =
+      static_cast<size_t>(tile_parameter_->in_shape_[mul_index] * tile_parameter_->in_strides_[mul_index]);
+    tile_parameter_->fast_outer_size_ =
+      static_cast<size_t>(in_tensors_.at(0)->ElementsNum()) / tile_parameter_->fast_stride_;
+  }
+  return;
+}
+
+int TileCPUKernel::SimpleTileImpl(int task_id) {
+  auto param = reinterpret_cast<TileParameter *>(op_parameter_);
+  MS_ASSERT(param);
+  size_t unit = UP_DIV(param->fast_outer_size_, static_cast<size_t>(context_->thread_num_));
+  if (unit == 0 && task_id > 0) {
+    return RET_OK;
+  }
+  size_t begin = unit * static_cast<size_t>(task_id);
+  size_t end = MSMIN(begin + unit, param->fast_outer_size_);
+  TileSimple(input_addr_, output_addr_, begin, end, param);
+  return RET_OK;
+}
+
+int TileCPUKernel::RunSimpleTile() {
+  auto ret = ParallelLaunch(context_->thread_pool_, SimpleTile, this, context_->thread_num_);
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "RunSimpleTile error code[" << ret << "]";
+    return ret;
+  }
+  return RET_OK;
+}
+
+int TileCPUKernel::Run() {
+  input_addr_ = reinterpret_cast<uint8_t *>(in_tensors_.at(0)->data_c());
+  output_addr_ = reinterpret_cast<uint8_t *>(out_tensors_.at(0)->data_c());
+  MS_ASSERT(input_addr_ != nullptr);
+  MS_ASSERT(output_addr_ != nullptr);
+  if (one_dim_tile_) {
+    return RunSimpleTile();
+  }
+  Tile(input_addr_, output_addr_, reinterpret_cast<TileParameter *>(op_parameter_));
+  return RET_OK;
+}
+
+REG_KERNEL(kCPU, kNumberTypeFloat32, PrimitiveType_Tile, LiteKernelCreator<TileCPUKernel>)
+REG_KERNEL(kCPU, kNumberTypeInt32, PrimitiveType_Tile, LiteKernelCreator<TileCPUKernel>)
+REG_KERNEL(kCPU, kNumberTypeFloat16, PrimitiveType_Tile, LiteKernelCreator<TileCPUKernel>)
+}  // namespace mindspore::kernel

mindspore/lite/src/runtime/kernel/arm/fp32/tile_fp32.h → mindspore/lite/src/runtime/kernel/arm/base/tile_base.h

@@ -13,12 +13,12 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#ifndef MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_TILE_H_
-#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_TILE_H_
+#ifndef MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_BASE_TILE_BASE_H_
+#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_BASE_TILE_BASE_H_
 
 #include <vector>
 #include "src/lite_kernel.h"
-#include "nnacl/fp32/tile_fp32.h"
+#include "nnacl/base/tile_base.h"
 
 namespace mindspore::kernel {
 class TileCPUKernel : public LiteKernel {
@@ -32,10 +32,16 @@ class TileCPUKernel : public LiteKernel {
   int Init() override;
   int ReSize() override;
   int Run() override;
+  int SimpleTileImpl(int task_id);
 
  private:
+  int RunSimpleTile();
   void ComputeStrides(const int *shape, int *strides, int ndim);
+  void FillOneDimTileParam();
+  bool one_dim_tile_;
+  uint8_t *input_addr_;
+  uint8_t *output_addr_;
 };
 }  // namespace mindspore::kernel
 
-#endif  // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_TILE_H_
+#endif  // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_BASE_TILE_BASE_H_

mindspore/lite/src/runtime/kernel/arm/fp16/transpose_fp16.cc

@@ -38,7 +38,21 @@ int TransposeFp16CPUKernel::Init() {
 }
 
 int TransposeFp16CPUKernel::Run() {
-  MS_ASSERT(in_tensors_.size() == 1);
+  MS_ASSERT(in_tensors_.size() == 1 || in_tensors_.size() == 2);
+  TransposeParameter *param = reinterpret_cast<TransposeParameter *>(this->op_parameter_);
+  if (in_tensors_.size() == 2) {
+    auto input_perm = in_tensors_.at(1);
+    MS_ASSERT(input_perm != nullptr);
+    MS_ASSERT(input_perm->data_c() != nullptr);
+    int *perm_data = reinterpret_cast<int *>(input_perm->data_c());
+    for (int i = 0; i < input_perm->ElementsNum(); ++i) {
+      param->perm_[i] = perm_data[i];
+    }
+    for (int i = input_perm->ElementsNum(); i < 8; ++i) {
+      param->perm_[i] = 0;
+    }
+    param->num_axes_ = input_perm->ElementsNum();
+  }
   MS_ASSERT(out_tensors_.size() == 1);
   auto &in_tensor = in_tensors_.front();
   auto &out_tensor = out_tensors_.front();
@@ -51,7 +65,6 @@ int TransposeFp16CPUKernel::Run() {
   MS_ASSERT(in_data_fp16_);
   MS_ASSERT(out_data_fp16_);
 
-  TransposeParameter *param = reinterpret_cast<TransposeParameter *>(this->op_parameter_);
   if (in_tensor->shape().size() != static_cast<size_t>(param->num_axes_)) {
     memcpy(out_data_fp16_, in_data_fp16_, in_tensor->ElementsNum() * sizeof(float16_t));
     return RET_OK;

mindspore/lite/src/runtime/kernel/arm/fp32/range_fp32.cc

@@ -68,6 +68,7 @@ int RangeCPUKernel::Run() {
   return RET_OK;
 }
 
+// fp16 may not be necessary because it involves small amount of data (input 3 number, output depends on input)
 REG_KERNEL(kCPU, kNumberTypeFloat32, PrimitiveType_Range, LiteKernelCreator<RangeCPUKernel>)
 REG_KERNEL(kCPU, kNumberTypeFloat, PrimitiveType_Range, LiteKernelCreator<RangeCPUKernel>)
 REG_KERNEL(kCPU, kNumberTypeInt32, PrimitiveType_Range, LiteKernelCreator<RangeCPUKernel>)

mindspore/lite/src/runtime/kernel/arm/fp32/tile_fp32.cc

@@ -1,80 +0,0 @@
-/**
- * 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 "src/runtime/kernel/arm/fp32/tile_fp32.h"
-#include "src/kernel_registry.h"
-#include "include/errorcode.h"
-
-using mindspore::lite::KernelRegistrar;
-using mindspore::lite::RET_ERROR;
-using mindspore::lite::RET_OK;
-using mindspore::schema::PrimitiveType_Tile;
-
-namespace mindspore::kernel {
-namespace {
-constexpr size_t kDoubleInputsSize = 2;
-}
-int TileCPUKernel::Init() {
-  if (!InferShapeDone()) {
-    return RET_OK;
-  }
-  return ReSize();
-}
-
-void TileCPUKernel::ComputeStrides(const int *shape, int *strides, int ndim) {
-  int stride = 1;
-  for (int i = ndim - 1; i >= 0; i--) {
-    strides[i] = stride;
-    stride *= shape[i];
-  }
-}
-
-int TileCPUKernel::ReSize() {
-  auto tile_parameter_ = reinterpret_cast<TileParameter *>(op_parameter_);
-  MS_ASSERT(tile_parameter_);
-  if (in_tensors_.size() == kDoubleInputsSize) {
-    if (in_tensors_[1]->ElementsNum() > static_cast<int>(in_tensors_[0]->shape().size())) {
-      MS_LOG(ERROR) << "tile's input1 data_num cannot be larger than input0's shape_size.";
-      return false;
-    }
-    auto input1_addr = reinterpret_cast<int *>(in_tensors_[1]->data_c());
-    for (int i = 0; i < in_tensors_[1]->ElementsNum(); ++i) {
-      tile_parameter_->dims_[i] = i;
-      tile_parameter_->multiples_[i] = input1_addr[i];
-    }
-  }
-  tile_parameter_->in_dim_ = in_tensors_.at(0)->shape().size();
-  for (int i = 0; i < tile_parameter_->in_dim_; ++i) {
-    tile_parameter_->in_shape_[i] = in_tensors_.at(0)->shape().at(i);
-    tile_parameter_->out_shape_[i] = out_tensors_.at(0)->shape().at(i);
-  }
-  ComputeStrides(tile_parameter_->in_shape_, tile_parameter_->in_strides_, tile_parameter_->in_dim_);
-  ComputeStrides(tile_parameter_->out_shape_, tile_parameter_->out_strides_, tile_parameter_->in_dim_);
-  return RET_OK;
-}
-
-int TileCPUKernel::Run() {
-  auto input_addr = reinterpret_cast<float *>(in_tensors_.at(0)->data_c());
-  auto output_addr = reinterpret_cast<float *>(out_tensors_.at(0)->data_c());
-  MS_ASSERT(input_addr);
-  MS_ASSERT(output_addr);
-  Tile(input_addr, output_addr, reinterpret_cast<TileParameter *>(op_parameter_));
-  return RET_OK;
-}
-
-REG_KERNEL(kCPU, kNumberTypeFloat32, PrimitiveType_Tile, LiteKernelCreator<TileCPUKernel>)
-REG_KERNEL(kCPU, kNumberTypeInt32, PrimitiveType_Tile, LiteKernelCreator<TileCPUKernel>)
-}  // namespace mindspore::kernel

mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/tile_fp32_tests.cc

@@ -14,10 +14,9 @@
  * limitations under the License.
  */
 
-#include <iostream>
 #include <memory>
 #include "common/common_test.h"
-#include "mindspore/lite/nnacl/fp32/tile_fp32.h"
+#include "mindspore/lite/nnacl/base/tile_base.h"
 #include "mindspore/lite/src/kernel_registry.h"
 
 namespace mindspore {
@@ -68,4 +67,94 @@ TEST_F(TestTileFp32, Tile) {
   in_tensor.set_data(nullptr);
   out_tensor.set_data(nullptr);
 }
+
+TEST_F(TestTileFp32, SimpleTile1) {
+  lite::Tensor in_tensor(kNumberTypeFloat32, {2, 2});
+  lite::Tensor out_tensor(kNumberTypeFloat32, {4, 2});
+  float input_data[] = {1, 2, 3, 4};
+  float output_data[8] = {0};
+  in_tensor.set_data(input_data);
+  out_tensor.set_data(output_data);
+  std::vector<lite::Tensor *> inputs = {&in_tensor};
+  std::vector<lite::Tensor *> outputs = {&out_tensor};
+
+  TileParameter parameter = {0};
+  parameter.in_dim_ = 2;
+  parameter.in_shape_[0] = 2;
+  parameter.in_shape_[1] = 2;
+  parameter.multiples_[0] = 2;
+  parameter.multiples_[1] = 1;
+  parameter.in_strides_[0] = 2;
+  parameter.in_strides_[1] = 1;
+  parameter.out_strides_[0] = 2;
+  parameter.out_strides_[1] = 1;
+
+  kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_Tile};
+
+  auto creator = lite::KernelRegistry::GetInstance()->GetCreator(desc);
+  EXPECT_NE(creator, nullptr);
+
+  auto ctx = std::make_shared<lite::InnerContext>();
+  ASSERT_EQ(lite::RET_OK, ctx->Init());
+  auto context = ctx.get();
+  context->thread_num_ = 2;
+  auto kernel = creator(inputs, outputs, reinterpret_cast<OpParameter *>(&parameter), context, desc, nullptr);
+  EXPECT_NE(kernel, nullptr);
+
+  auto ret = kernel->Run();
+  EXPECT_EQ(0, ret);
+
+  float expect[] = {1, 2, 3, 4, 1, 2, 3, 4};
+  for (int i = 0; i < 8; ++i) {
+    EXPECT_EQ(output_data[i], expect[i]);
+  }
+
+  in_tensor.set_data(nullptr);
+  out_tensor.set_data(nullptr);
+}
+
+TEST_F(TestTileFp32, SimpleTile2) {
+  lite::Tensor in_tensor(kNumberTypeFloat32, {2, 2});
+  lite::Tensor out_tensor(kNumberTypeFloat32, {2, 4});
+  float input_data[] = {1, 2, 3, 4};
+  float output_data[8] = {0};
+  in_tensor.set_data(input_data);
+  out_tensor.set_data(output_data);
+  std::vector<lite::Tensor *> inputs = {&in_tensor};
+  std::vector<lite::Tensor *> outputs = {&out_tensor};
+
+  TileParameter parameter = {0};
+  parameter.in_dim_ = 2;
+  parameter.in_shape_[0] = 2;
+  parameter.in_shape_[1] = 2;
+  parameter.multiples_[0] = 1;
+  parameter.multiples_[1] = 2;
+  parameter.in_strides_[0] = 2;
+  parameter.in_strides_[1] = 1;
+  parameter.out_strides_[0] = 4;
+  parameter.out_strides_[1] = 1;
+
+  kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_Tile};
+
+  auto creator = lite::KernelRegistry::GetInstance()->GetCreator(desc);
+  EXPECT_NE(creator, nullptr);
+
+  auto ctx = std::make_shared<lite::InnerContext>();
+  ASSERT_EQ(lite::RET_OK, ctx->Init());
+  auto context = ctx.get();
+  context->thread_num_ = 2;
+  auto kernel = creator(inputs, outputs, reinterpret_cast<OpParameter *>(&parameter), context, desc, nullptr);
+  EXPECT_NE(kernel, nullptr);
+
+  auto ret = kernel->Run();
+  EXPECT_EQ(0, ret);
+
+  float expect[] = {1, 2, 1, 2, 3, 4, 3, 4};
+  for (int i = 0; i < 8; ++i) {
+    EXPECT_EQ(output_data[i], expect[i]);
+  }
+
+  in_tensor.set_data(nullptr);
+  out_tensor.set_data(nullptr);
+}
 }  // namespace mindspore