!6998 [MSLITE][Develop] Refactor arithmetic_self and add fp16 kernel

Merge pull request !6998 from sunsuodong/arithmetic_self_fp16

mindspore/lite/nnacl/fp16/arithmetic_self_fp16.c

@@ -0,0 +1,110 @@
+/**
+ * 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 <math.h>
+#include "nnacl/fp16/arithmetic_self_fp16.h"
+
+int ElementAbsFp16(float16_t *input, float16_t *output, int element_size) {
+  for (int i = 0; i < element_size; i++) {
+    output[i] = fabsf(input[i]);
+  }
+  return NNACL_OK;
+}
+
+int ElementCosFp16(float16_t *input, float16_t *output, int element_size) {
+  for (int i = 0; i < element_size; i++) {
+    output[i] = cosf(input[i]);
+  }
+  return NNACL_OK;
+}
+
+int ElementLogFp16(float16_t *input, float16_t *output, int element_size) {
+  for (int i = 0; i < element_size; i++) {
+    if (input[i] <= 0) {
+      return NNACL_ERRCODE_LOG_NEGATIVE_OR_ZERO;
+    }
+    output[i] = logf(input[i]);
+  }
+  return NNACL_OK;
+}
+
+int ElementSquareFp16(float16_t *input, float16_t *output, int element_size) {
+  for (int i = 0; i < element_size; i++) {
+    output[i] = input[i] * input[i];
+  }
+  return NNACL_OK;
+}
+
+int ElementSqrtFp16(float16_t *input, float16_t *output, int element_size) {
+  for (int i = 0; i < element_size; i++) {
+    if (input[i] < 0) {
+      return NNACL_ERRCODE_SQRT_NEGATIVE;
+    }
+    output[i] = sqrtf(input[i]);
+  }
+  return NNACL_OK;
+}
+
+int ElementRsqrtFp16(float16_t *input, float16_t *output, int element_size) {
+  for (int i = 0; i < element_size; i++) {
+    if (input[i] <= 0) {
+      return NNACL_ERRCODE_RSQRT_NEGATIVE_OR_ZERO;
+    }
+    output[i] = 1.f / sqrtf(input[i]);
+  }
+  return NNACL_OK;
+}
+
+int ElementSinFp16(float16_t *input, float16_t *output, int element_size) {
+  for (int i = 0; i < element_size; i++) {
+    output[i] = sinf(input[i]);
+  }
+  return NNACL_OK;
+}
+
+int ElementLogicalNotFp16(float16_t *input, float16_t *output, int element_size) {
+  for (int i = 0; i < element_size; i++) {
+    output[i] = (float)(!((bool)(input[i])));
+  }
+  return NNACL_OK;
+}
+
+int ElementRoundFp16(float16_t *input, float16_t *output, int element_size) {
+  for (int i = 0; i < element_size; i++) {
+    output[i] = round(input[i]);
+  }
+  return NNACL_OK;
+}
+
+int ElementFloorFp16(float16_t *input, float16_t *output, int element_size) {
+  for (int i = 0; i < element_size; i++) {
+    output[i] = floorf(input[i]);
+  }
+  return NNACL_OK;
+}
+
+int ElementCeilFp16(float16_t *input, float16_t *output, int number) {
+  for (int i = 0; i < number; ++i) {
+    output[i] = ceil(input[i]);
+  }
+  return NNACL_OK;
+}
+
+int ElementNegativeFp16(float16_t *input, float16_t *output, int element_size) {
+  for (int i = 0; i < element_size; ++i) {
+    output[i] = -input[i];
+  }
+  return NNACL_OK;
+}

mindspore/lite/nnacl/fp16/arithmetic_self_fp16.h

@@ -0,0 +1,55 @@
+/**
+ * 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_LITE_NNACL_FP16_ARITHMETIC_SELF_FP16_H_
+#define MINDSPORE_LITE_NNACL_FP16_ARITHMETIC_SELF_FP16_H_
+
+#ifdef ENABLE_NEON
+#include <arm_neon.h>
+#endif
+#include "nnacl/op_base.h"
+#include "nnacl/errorcode.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+int ElementAbsFp16(float16_t *input, float16_t *output, int element_size);
+
+int ElementCosFp16(float16_t *input, float16_t *output, int element_size);
+
+int ElementLogFp16(float16_t *input, float16_t *output, int element_size);
+
+int ElementSquareFp16(float16_t *input, float16_t *output, int element_size);
+
+int ElementSqrtFp16(float16_t *input, float16_t *output, int element_size);
+
+int ElementRsqrtFp16(float16_t *input, float16_t *output, int element_size);
+
+int ElementSinFp16(float16_t *input, float16_t *output, int element_size);
+
+int ElementLogicalNotFp16(float16_t *input, float16_t *output, int element_size);
+
+int ElementRoundFp16(float16_t *input, float16_t *output, int element_size);
+
+int ElementFloorFp16(float16_t *input, float16_t *output, int element_size);
+
+int ElementCeilFp16(float16_t *input, float16_t *output, int number);
+
+int ElementNegativeFp16(float16_t *input, float16_t *output, int element_size);
+#ifdef __cplusplus
+}
+#endif
+
+#endif  // MINDSPORE_LITE_NNACL_FP16_ARITHMETIC_SELF_FP16_H_

mindspore/lite/src/runtime/kernel/arm/fp16/arithmetic_self_fp16.cc

@@ -0,0 +1,142 @@
+/**
+ * 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/fp16/arithmetic_self_fp16.h"
+#include "src/runtime/kernel/arm/fp16/common_fp16.h"
+#include "src/kernel_registry.h"
+#include "nnacl/fp16/cast_fp16.h"
+#include "nnacl/fp16/arithmetic_self_fp16.h"
+
+using mindspore::lite::KernelRegistrar;
+
+namespace mindspore::kernel {
+namespace {
+typedef struct {
+  int primitive_type_;
+  ArithmeticSelfFp16Func func_;
+} TYPE_FUNC_INFO;
+}  // namespace
+
+ArithmeticSelfFp16Func ArithmeticSelfFp16CPUKernel::GetArithmeticSelfFp16Fun(int primitive_type) {
+  TYPE_FUNC_INFO type_func_table[] = {{mindspore::schema::PrimitiveType_Abs, ElementAbsFp16},
+                                      {mindspore::schema::PrimitiveType_Cos, ElementCosFp16},
+                                      {mindspore::schema::PrimitiveType_Log, ElementLogFp16},
+                                      {mindspore::schema::PrimitiveType_Square, ElementSquareFp16},
+                                      {mindspore::schema::PrimitiveType_Sqrt, ElementSqrtFp16},
+                                      {mindspore::schema::PrimitiveType_Rsqrt, ElementRsqrtFp16},
+                                      {mindspore::schema::PrimitiveType_Sin, ElementSinFp16},
+                                      {mindspore::schema::PrimitiveType_LogicalNot, ElementLogicalNotFp16},
+                                      {mindspore::schema::PrimitiveType_Floor, ElementFloorFp16},
+                                      {mindspore::schema::PrimitiveType_Ceil, ElementCeilFp16},
+                                      {mindspore::schema::PrimitiveType_Round, ElementRoundFp16},
+                                      {mindspore::schema::PrimitiveType_Neg, ElementNegativeFp16}};
+  for (size_t i = 0; i < sizeof(type_func_table); i++) {
+    if (type_func_table[i].primitive_type_ == primitive_type) {
+      return type_func_table[i].func_;
+    }
+  }
+  return nullptr;
+}
+
+int ArithmeticSelfFp16CPUKernel::DoExecute(int task_id) {
+  int elements_num = in_tensors_.at(0)->ElementsNum();
+  int stride = UP_DIV(elements_num, op_parameter_->thread_num_);
+  int offset = task_id * stride;
+  int count = MSMIN(stride, elements_num - offset);
+  if (count <= 0) {
+    return RET_OK;
+  }
+  if (fp16_func_ == nullptr) {
+    MS_LOG(ERROR) << "Run function is null! ";
+    return RET_ERROR;
+  }
+  auto ret = fp16_func_(input_fp16_ptr_ + offset, output_fp16_ptr_ + offset, count);
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "Run failed, illegal input! ";
+  }
+  return ret;
+}
+
+void ArithmeticSelfFp16CPUKernel::FreeInputAndOutput() {
+  if (in_tensors_.at(0)->data_type() == kNumberTypeFloat32) {
+    context_->allocator->Free(input_fp16_ptr_);
+    input_fp16_ptr_ = nullptr;
+  }
+  if (out_tensors_.at(0)->data_type() == kNumberTypeFloat32) {
+    context_->allocator->Free(output_fp16_ptr_);
+    output_fp16_ptr_ = nullptr;
+  }
+}
+
+int ArithmeticSelfFp16CPUKernel::Run() {
+  auto ret = Prepare();
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "Prepare fail! ret: " << ret;
+    return ret;
+  }
+  auto input_tensor = in_tensors_.at(0);
+  auto output_tensor = out_tensors_.at(0);
+  input_fp16_ptr_ = ConvertInputFp32toFp16(input_tensor, context_);
+  output_fp16_ptr_ = MallocOutputFp16(output_tensor, context_);
+  if (input_fp16_ptr_ == nullptr || output_fp16_ptr_ == nullptr) {
+    FreeInputAndOutput();
+    MS_LOG(ERROR) << "input or output is nullptr";
+    return RET_ERROR;
+  }
+  ret = ParallelLaunch(this->context_->thread_pool_, ArithmeticSelfRun, this, op_parameter_->thread_num_);
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "ArithmeticSelfRun error error_code[" << ret << "]";
+  }
+  if (out_tensors_.at(0)->data_type() == kNumberTypeFloat32) {
+    Float16ToFloat32(output_fp16_ptr_, reinterpret_cast<float *>(output_tensor->MutableData()),
+                     output_tensor->ElementsNum());
+  }
+  FreeInputAndOutput();
+  return ret;
+}
+
+kernel::LiteKernel *CpuArithmeticSelfFp16KernelCreator(const std::vector<lite::Tensor *> &inputs,
+                                                       const std::vector<lite::Tensor *> &outputs,
+                                                       OpParameter *parameter, const lite::InnerContext *ctx,
+                                                       const kernel::KernelKey &desc,
+                                                       const mindspore::lite::PrimitiveC *primitive) {
+  auto *kernel = new (std::nothrow) ArithmeticSelfFp16CPUKernel(parameter, inputs, outputs, ctx, primitive);
+  if (kernel == nullptr) {
+    MS_LOG(ERROR) << "new ArithmeticSelfFp16CPUKernel fail!";
+    return nullptr;
+  }
+  auto ret = kernel->Init();
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "Init kernel failed, name: " << parameter->name_
+                  << ", type: " << schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(parameter->type_));
+    delete kernel;
+    return nullptr;
+  }
+  return kernel;
+}
+
+REG_KERNEL(kCPU, kNumberTypeFloat16, PrimitiveType_Abs, CpuArithmeticSelfFp16KernelCreator)
+REG_KERNEL(kCPU, kNumberTypeFloat16, PrimitiveType_Cos, CpuArithmeticSelfFp16KernelCreator)
+REG_KERNEL(kCPU, kNumberTypeFloat16, PrimitiveType_Log, CpuArithmeticSelfFp16KernelCreator)
+REG_KERNEL(kCPU, kNumberTypeFloat16, PrimitiveType_Square, CpuArithmeticSelfFp16KernelCreator)
+REG_KERNEL(kCPU, kNumberTypeFloat16, PrimitiveType_Sqrt, CpuArithmeticSelfFp16KernelCreator)
+REG_KERNEL(kCPU, kNumberTypeFloat16, PrimitiveType_Rsqrt, CpuArithmeticSelfFp16KernelCreator)
+REG_KERNEL(kCPU, kNumberTypeFloat16, PrimitiveType_Sin, CpuArithmeticSelfFp16KernelCreator)
+REG_KERNEL(kCPU, kNumberTypeFloat16, PrimitiveType_LogicalNot, CpuArithmeticSelfFp16KernelCreator)
+REG_KERNEL(kCPU, kNumberTypeFloat16, PrimitiveType_Floor, CpuArithmeticSelfFp16KernelCreator)
+REG_KERNEL(kCPU, kNumberTypeFloat16, PrimitiveType_Ceil, CpuArithmeticSelfFp16KernelCreator)
+REG_KERNEL(kCPU, kNumberTypeFloat16, PrimitiveType_Round, CpuArithmeticSelfFp16KernelCreator)
+REG_KERNEL(kCPU, kNumberTypeFloat16, PrimitiveType_Neg, CpuArithmeticSelfFp16KernelCreator)
+}  // namespace mindspore::kernel

mindspore/lite/src/runtime/kernel/arm/fp16/arithmetic_self_fp16.h

@@ -0,0 +1,46 @@
+/**
+ * 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_LITE_SRC_RUNTIME_KERNEL_ARM_FP16_ARITHMETIC_SELF_FP16_H_
+#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP16_ARITHMETIC_SELF_FP16_H_
+
+#include <vector>
+#include "src/runtime/kernel/arm/fp32/arithmetic_self.h"
+
+namespace mindspore::kernel {
+typedef int (*ArithmeticSelfFp16Func)(float16_t *input, float16_t *output, int element_size);
+class ArithmeticSelfFp16CPUKernel : public ArithmeticSelfCPUKernel {
+ public:
+  explicit ArithmeticSelfFp16CPUKernel(OpParameter *parameter, const std::vector<lite::Tensor *> &inputs,
+                                       const std::vector<lite::Tensor *> &outputs, const lite::InnerContext *ctx,
+                                       const mindspore::lite::PrimitiveC *primitive)
+      : ArithmeticSelfCPUKernel(parameter, inputs, outputs, ctx, primitive) {
+    fp16_func_ = GetArithmeticSelfFp16Fun(parameter->type_);
+  }
+  ~ArithmeticSelfFp16CPUKernel() override = default;
+
+  int Run() override;
+  int DoExecute(int task_id) override;
+
+ private:
+  void FreeInputAndOutput();
+  ArithmeticSelfFp16Func GetArithmeticSelfFp16Fun(int primitive_type);
+  ArithmeticSelfFp16Func fp16_func_ = nullptr;
+  float16_t *input_fp16_ptr_ = nullptr;
+  float16_t *output_fp16_ptr_ = nullptr;
+};
+}  // namespace mindspore::kernel
+
+#endif  // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP16_ARITHMETIC_SELF_FP16_H_

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

@@ -13,99 +13,107 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 #include "src/runtime/kernel/arm/fp32/arithmetic_self.h"
-#include "schema/model_generated.h"
 #include "src/kernel_registry.h"
-#include "include/errorcode.h"
-#include "src/runtime/runtime_api.h"
+#include "nnacl/fp32/arithmetic_self.h"
 
-using mindspore::kernel::KERNEL_ARCH::kCPU;
 using mindspore::lite::KernelRegistrar;
-using mindspore::lite::RET_ERROR;
-using mindspore::lite::RET_OK;
 
 namespace mindspore::kernel {
+namespace {
+typedef struct {
+  int primitive_type_;
+  ArithmeticSelfFunc func_;
+} TYPE_FUNC_INFO;
+}  // namespace
+
+ArithmeticSelfFunc ArithmeticSelfCPUKernel::GetArithmeticSelfFun(int primitive_type) {
+  TYPE_FUNC_INFO type_func_table[] = {{mindspore::schema::PrimitiveType_Abs, ElementAbs},
+                                      {mindspore::schema::PrimitiveType_Cos, ElementCos},
+                                      {mindspore::schema::PrimitiveType_Log, ElementLog},
+                                      {mindspore::schema::PrimitiveType_Square, ElementSquare},
+                                      {mindspore::schema::PrimitiveType_Sqrt, ElementSqrt},
+                                      {mindspore::schema::PrimitiveType_Rsqrt, ElementRsqrt},
+                                      {mindspore::schema::PrimitiveType_Sin, ElementSin},
+                                      {mindspore::schema::PrimitiveType_LogicalNot, ElementLogicalNot},
+                                      {mindspore::schema::PrimitiveType_Floor, ElementFloor},
+                                      {mindspore::schema::PrimitiveType_Ceil, ElementCeil},
+                                      {mindspore::schema::PrimitiveType_Round, ElementRound},
+                                      {mindspore::schema::PrimitiveType_Neg, ElementNegative}};
+  for (size_t i = 0; i < sizeof(type_func_table); i++) {
+    if (type_func_table[i].primitive_type_ == primitive_type) {
+      return type_func_table[i].func_;
+    }
+  }
+  return nullptr;
+}
+
 int ArithmeticSelfCPUKernel::Init() {
   if (!InferShapeDone()) {
     return RET_OK;
   }
-
   return ReSize();
 }
 
-int ArithmeticSelfCPUKernel::ReSize() {
-  data_size_ = in_tensors_[0]->ElementsNum();
-  thread_sz_count_ = MSMIN(thread_count_, static_cast<int>(data_size_));
-  thread_sz_stride_ = UP_DIV(data_size_, thread_sz_count_);
-  return RET_OK;
-}
-
-int ArithmeticSelfRuns(void *cdata, int task_id) {
-  auto g_kernel = reinterpret_cast<ArithmeticSelfCPUKernel *>(cdata);
-  auto ret = g_kernel->DoArithmeticSelf(task_id);
-  if (ret != RET_OK) {
-    MS_LOG(ERROR) << "ArithmeticSelfRuns error task_id[" << task_id << "] error_code[" << ret << "]";
-    return ret;
-  }
-  return RET_OK;
-}
+int ArithmeticSelfCPUKernel::ReSize() { return RET_OK; }
 
-int ArithmeticSelfCPUKernel::DoArithmeticSelf(int task_id) {
-  int size = MSMIN(thread_sz_stride_, static_cast<int>(data_size_ - task_id * thread_sz_stride_));
-  if (size <= 0) {
+int ArithmeticSelfCPUKernel::DoExecute(int task_id) {
+  int elements_num = in_tensors_.at(0)->ElementsNum();
+  int stride = UP_DIV(elements_num, op_parameter_->thread_num_);
+  int offset = task_id * stride;
+  int count = MSMIN(stride, elements_num - offset);
+  if (count <= 0) {
     return RET_OK;
   }
-  int offset = task_id * thread_sz_stride_;
-  if (arithmeticSelf_run_) {
-    auto ret = arithmeticSelf_run_(in_ptr_ + offset, out_ptr_ + offset, size);
-    if (ret != RET_OK) {
-      MS_LOG(ERROR) << "Run failed, illegal input! ";
-      return ret;
-    }
-  } else {
+  if (func_ == nullptr) {
     MS_LOG(ERROR) << "Run function is null! ";
     return RET_ERROR;
   }
-  return RET_OK;
+  float *input_ptr = reinterpret_cast<float *>(in_tensors_.at(0)->MutableData());
+  float *output_ptr = reinterpret_cast<float *>(out_tensors_.at(0)->MutableData());
+  auto ret = func_(input_ptr + offset, output_ptr + offset, count);
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "Run failed, illegal input! ";
+  }
+  return ret;
 }
+
+int ArithmeticSelfRun(void *cdata, int task_id) {
+  auto kernel = reinterpret_cast<ArithmeticSelfCPUKernel *>(cdata);
+  auto ret = kernel->DoExecute(task_id);
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "ArithmeticSelfRuns error task_id[" << task_id << "] error_code[" << ret << "]";
+  }
+  return ret;
+}
+
 int ArithmeticSelfCPUKernel::Run() {
   auto ret = Prepare();
   if (ret != RET_OK) {
-    MS_LOG(ERROR) << "Prepare fail!ret: " << ret;
+    MS_LOG(ERROR) << "Prepare fail! ret: " << ret;
     return ret;
   }
-  auto input_tensor = in_tensors_.at(0);
-  auto out_tensor = out_tensors_.at(0);
-  in_ptr_ = reinterpret_cast<float *>(input_tensor->MutableData());
-  out_ptr_ = reinterpret_cast<float *>(out_tensor->MutableData());
-  ret = ParallelLaunch(this->context_->thread_pool_, ArithmeticSelfRuns, this, thread_sz_count_);
+  ret = ParallelLaunch(this->context_->thread_pool_, ArithmeticSelfRun, this, op_parameter_->thread_num_);
   if (ret != RET_OK) {
     MS_LOG(ERROR) << "ArithmeticSelfRun error error_code[" << ret << "]";
-    return ret;
   }
-  return RET_OK;
+  return ret;
 }
 
 kernel::LiteKernel *CpuArithmeticSelfFp32KernelCreator(const std::vector<lite::Tensor *> &inputs,
                                                        const std::vector<lite::Tensor *> &outputs,
-                                                       OpParameter *opParameter, const lite::InnerContext *ctx,
+                                                       OpParameter *parameter, const lite::InnerContext *ctx,
                                                        const kernel::KernelKey &desc,
                                                        const mindspore::lite::PrimitiveC *primitive) {
-  MS_ASSERT(opParameter != nullptr);
-  if (opParameter == nullptr) {
-    MS_LOG(ERROR) << "Creator failed, opParameter is nullptr!";
-    return nullptr;
-  }
-  auto *kernel = new (std::nothrow) ArithmeticSelfCPUKernel(opParameter, inputs, outputs, ctx, primitive);
+  auto *kernel = new (std::nothrow) ArithmeticSelfCPUKernel(parameter, inputs, outputs, ctx, primitive);
   if (kernel == nullptr) {
     MS_LOG(ERROR) << "new ArithmeticSelfCPUKernel fail!";
     return nullptr;
   }
   auto ret = kernel->Init();
   if (ret != RET_OK) {
-    MS_LOG(ERROR) << "Init kernel failed, name: " << opParameter->name_ << ", type: "
-                  << schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(opParameter->type_));
+    MS_LOG(ERROR) << "Init kernel failed, name: " << parameter->name_
+                  << ", type: " << schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(parameter->type_));
     delete kernel;
     return nullptr;
   }

mindspore/lite/src/runtime/kernel/arm/fp32/arithmetic_self.h

@@ -13,18 +13,12 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 #ifndef MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_ARITHMETIC_SELF_H_
 #define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_ARITHMETIC_SELF_H_
 
 #include <vector>
 #include "src/lite_kernel.h"
-#include "nnacl/fp32/arithmetic_self.h"
-#include "nnacl/arithmetic_self_parameter.h"
-#include "schema/model_generated.h"
-#include "include/context.h"
 
-using mindspore::lite::InnerContext;
 using mindspore::schema::PrimitiveType_Abs;
 using mindspore::schema::PrimitiveType_Ceil;
 using mindspore::schema::PrimitiveType_Cos;
@@ -39,73 +33,27 @@ using mindspore::schema::PrimitiveType_Sqrt;
 using mindspore::schema::PrimitiveType_Square;
 
 namespace mindspore::kernel {
+typedef int (*ArithmeticSelfFunc)(float *input, float *output, int element_size);
 class ArithmeticSelfCPUKernel : public LiteKernel {
-  typedef int (*ArithmeticSelfRun)(float *input, float *output, int element_size);
-
  public:
   explicit ArithmeticSelfCPUKernel(OpParameter *parameter, const std::vector<lite::Tensor *> &inputs,
                                    const std::vector<lite::Tensor *> &outputs, const lite::InnerContext *ctx,
                                    const mindspore::lite::PrimitiveC *primitive)
-      : LiteKernel(parameter, inputs, outputs, ctx, primitive), thread_count_(ctx->thread_num_) {
-    switch (parameter->type_) {
-      case PrimitiveType_Abs:
-        arithmeticSelf_run_ = ElementAbs;
-        break;
-      case PrimitiveType_Cos:
-        arithmeticSelf_run_ = ElementCos;
-        break;
-      case PrimitiveType_Log:
-        arithmeticSelf_run_ = ElementLog;
-        break;
-      case PrimitiveType_Square:
-        arithmeticSelf_run_ = ElementSquare;
-        break;
-      case PrimitiveType_Sqrt:
-        arithmeticSelf_run_ = ElementSqrt;
-        break;
-      case PrimitiveType_Rsqrt:
-        arithmeticSelf_run_ = ElementRsqrt;
-        break;
-      case PrimitiveType_Sin:
-        arithmeticSelf_run_ = ElementSin;
-        break;
-      case PrimitiveType_LogicalNot:
-        arithmeticSelf_run_ = ElementLogicalNot;
-        break;
-      case PrimitiveType_Floor:
-        arithmeticSelf_run_ = ElementFloor;
-        break;
-      case PrimitiveType_Ceil:
-        arithmeticSelf_run_ = ElementCeil;
-        break;
-      case PrimitiveType_Round:
-        arithmeticSelf_run_ = ElementRound;
-        break;
-      case PrimitiveType_Neg:
-        arithmeticSelf_run_ = ElementNegative;
-        break;
-      default:
-        break;
-    }
-    arithmeticSelfParameter_ = reinterpret_cast<ArithmeticSelfParameter *>(parameter);
+      : LiteKernel(parameter, inputs, outputs, ctx, primitive) {
+    func_ = GetArithmeticSelfFun(parameter->type_);
   }
   ~ArithmeticSelfCPUKernel() override = default;
 
   int Init() override;
   int ReSize() override;
   int Run() override;
-  int DoArithmeticSelf(int task_id);
+  virtual int DoExecute(int task_id);
 
  private:
-  int thread_sz_count_;
-  int thread_sz_stride_;
-  size_t data_size_;
-  ArithmeticSelfParameter *arithmeticSelfParameter_;
-  ArithmeticSelfRun arithmeticSelf_run_;
-  int thread_count_;
-  float *in_ptr_;
-  float *out_ptr_;
+  ArithmeticSelfFunc GetArithmeticSelfFun(int primitive_type);
+  ArithmeticSelfFunc func_;
 };
+int ArithmeticSelfRun(void *cdata, int task_id);
 }  // namespace mindspore::kernel
 
 #endif  // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_ARITHMETIC_SELF_H_