!10218 add CPU ops: Sign, NotEqual, Split, AvgPool, AvgPoolGradCpu

From: @caojian05
Reviewed-by: 
Signed-off-by:

mindspore/ccsrc/backend/kernel_compiler/cpu/arithmetic_cpu_kernel.cc

@@ -99,6 +99,24 @@ void ArithmeticCPUKernel::Less(const T *input1, const T *input2, bool *out, size
   }
 }
 
+template <typename T>
+void ArithmeticCPUKernel::Equal(const T *input1, const T *input2, bool *out, size_t start, size_t end) {
+  for (size_t i = start; i < end; i++) {
+    std::vector<size_t> idx;
+    GenIndex(i, &idx);
+    out[i] = input1[idx[0]] == input2[idx[1]];
+  }
+}
+
+template <typename T>
+void ArithmeticCPUKernel::NotEqual(const T *input1, const T *input2, bool *out, size_t start, size_t end) {
+  for (size_t i = start; i < end; i++) {
+    std::vector<size_t> idx;
+    GenIndex(i, &idx);
+    out[i] = input1[idx[0]] != input2[idx[1]];
+  }
+}
+
 void ArithmeticCPUKernel::InitKernel(const CNodePtr &kernel_node) {
   MS_EXCEPTION_IF_NULL(kernel_node);
   std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node);
@@ -114,6 +132,10 @@ void ArithmeticCPUKernel::InitKernel(const CNodePtr &kernel_node) {
     operate_type_ = POW;
   } else if (kernel_name == prim::kPrimLess->name()) {
     operate_type_ = LESS;
+  } else if (kernel_name == prim::kPrimEqual->name()) {
+    operate_type_ = EQUAL;
+  } else if (kernel_name == prim::kPrimNotEqual->name()) {
+    operate_type_ = NOTEQUAL;
   } else if (kernel_name == prim::kPrimAssignAdd->name()) {
     operate_type_ = ASSIGNADD;
   } else {
@@ -141,19 +163,22 @@ void ArithmeticCPUKernel::InitKernel(const CNodePtr &kernel_node) {
   if (dtype_ != AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 1)) {
     MS_LOG(EXCEPTION) << "Input0 and input1 must has the same data type";
   }
+  target_dtype_ = AnfAlgo::GetOutputInferDataType(kernel_node, 0);
 }
 
 bool ArithmeticCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
                                  const std::vector<kernel::AddressPtr> & /*workspace*/,
                                  const std::vector<kernel::AddressPtr> &outputs) {
-  if (dtype_ == kNumberTypeInt32 || dtype_ == kNumberTypeInt16) {
+  if (dtype_ == kNumberTypeInt32 || dtype_ == kNumberTypeInt16 || dtype_ == kNumberTypeInt8) {
     LaunchKernel<int>(inputs, outputs);
   } else if (dtype_ == kNumberTypeFloat32 || dtype_ == kNumberTypeFloat16 || dtype_ == kNumberTypeFloat64) {
     LaunchKernel<float>(inputs, outputs);
   } else if (dtype_ == kNumberTypeInt64) {
     LaunchKernel<int64_t>(inputs, outputs);
+  } else if (dtype_ == kNumberTypeBool) {
+    LaunchKernelLogic<bool>(inputs, outputs);
   } else {
-    MS_LOG(EXCEPTION) << "Data type is " << TypeIdLabel(dtype_) << "is not support.";
+    MS_LOG(EXCEPTION) << "Data type " << TypeIdLabel(dtype_) << "is not support.";
   }
   return true;
 }
@@ -190,7 +215,8 @@ void ArithmeticCPUKernel::GenIndex(size_t num, std::vector<size_t> *idx) {
 }
 
 template <typename T>
-void ArithmeticCPUKernel::LaunchLess(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &outputs) {
+void ArithmeticCPUKernel::LaunchKernelLogic(const std::vector<AddressPtr> &inputs,
+                                            const std::vector<AddressPtr> &outputs) {
   T *input1 = reinterpret_cast<T *>(inputs[0]->addr);
   T *input2 = reinterpret_cast<T *>(inputs[1]->addr);
   bool *output = reinterpret_cast<bool *>(outputs[0]->addr);
@@ -213,7 +239,15 @@ void ArithmeticCPUKernel::LaunchLess(const std::vector<AddressPtr> &inputs, cons
   }
   while (start < lens) {
     size_t end = (start + once_compute_size) > lens ? lens : (start + once_compute_size);
-    threads.emplace_back(std::thread(&ArithmeticCPUKernel::Less<T>, this, input1, input2, output, start, end));
+    if (operate_type_ == LESS) {
+      threads.emplace_back(std::thread(&ArithmeticCPUKernel::Less<T>, this, input1, input2, output, start, end));
+    } else if (operate_type_ == EQUAL) {
+      threads.emplace_back(std::thread(&ArithmeticCPUKernel::Equal<T>, this, input1, input2, output, start, end));
+    } else if (operate_type_ == NOTEQUAL) {
+      threads.emplace_back(std::thread(&ArithmeticCPUKernel::NotEqual<T>, this, input1, input2, output, start, end));
+    } else {
+      MS_LOG(EXCEPTION) << "Not support " << operate_type_;
+    }
     start += once_compute_size;
   }
   for (size_t i = 0; i < threads.size(); ++i) {
@@ -223,8 +257,8 @@ void ArithmeticCPUKernel::LaunchLess(const std::vector<AddressPtr> &inputs, cons
 
 template <typename T>
 void ArithmeticCPUKernel::LaunchKernel(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &outputs) {
-  if (operate_type_ == LESS) {
-    LaunchLess<T>(inputs, outputs);
+  if (target_dtype_ == kNumberTypeBool) {
+    LaunchKernelLogic<T>(inputs, outputs);
     return;
   }
   T *input1 = reinterpret_cast<T *>(inputs[0]->addr);

mindspore/ccsrc/backend/kernel_compiler/cpu/arithmetic_cpu_kernel.h

@@ -17,6 +17,7 @@
 #define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_ARITHMETIC_CPU_KERNEL_H_
 #include <memory>
 #include <vector>
+#include <limits>
 #include "backend/kernel_compiler/cpu/cpu_kernel.h"
 #include "backend/kernel_compiler/cpu/cpu_kernel_factory.h"
 
@@ -32,7 +33,7 @@ class ArithmeticCPUKernel : public CPUKernel {
   bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
               const std::vector<AddressPtr> &outputs) override;
   template <typename T>
-  void LaunchLess(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &outputs);
+  void LaunchKernelLogic(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &outputs);
   template <typename T>
   void LaunchKernel(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &outputs);
 
@@ -52,6 +53,10 @@ class ArithmeticCPUKernel : public CPUKernel {
   void AssignAdd(T *input1, const T *input2, T *out, size_t start, size_t end);
   template <typename T>
   void Less(const T *input1, const T *input2, bool *out, size_t start, size_t end);
+  template <typename T>
+  void Equal(const T *input1, const T *input2, bool *out, size_t start, size_t end);
+  template <typename T>
+  void NotEqual(const T *input1, const T *input2, bool *out, size_t start, size_t end);
   std::vector<size_t> input_shape0_;
   std::vector<size_t> input_shape1_;
   std::vector<size_t> input_element_num0_;
@@ -60,6 +65,7 @@ class ArithmeticCPUKernel : public CPUKernel {
   std::vector<size_t> output_element_num_;
   OperateType operate_type_{ADD};
   TypeId dtype_{kTypeUnknown};
+  TypeId target_dtype_{kTypeUnknown};
 };
 
 MS_REG_CPU_KERNEL(
@@ -108,6 +114,70 @@ MS_REG_CPU_KERNEL(
 MS_REG_CPU_KERNEL(
   Mul, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
   ArithmeticCPUKernel);
+MS_REG_CPU_KERNEL(
+  Equal, KernelAttr().AddInputAttr(kNumberTypeBool).AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool),
+  ArithmeticCPUKernel);
+MS_REG_CPU_KERNEL(
+  Equal, KernelAttr().AddInputAttr(kNumberTypeInt8).AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeBool),
+  ArithmeticCPUKernel);
+MS_REG_CPU_KERNEL(
+  Equal, KernelAttr().AddInputAttr(kNumberTypeInt16).AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeBool),
+  ArithmeticCPUKernel);
+MS_REG_CPU_KERNEL(
+  Equal, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeBool),
+  ArithmeticCPUKernel);
+MS_REG_CPU_KERNEL(
+  Equal, KernelAttr().AddInputAttr(kNumberTypeUInt8).AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeBool),
+  ArithmeticCPUKernel);
+MS_REG_CPU_KERNEL(
+  Equal, KernelAttr().AddInputAttr(kNumberTypeUInt16).AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeBool),
+  ArithmeticCPUKernel);
+MS_REG_CPU_KERNEL(
+  Equal, KernelAttr().AddInputAttr(kNumberTypeUInt32).AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeBool),
+  ArithmeticCPUKernel);
+MS_REG_CPU_KERNEL(
+  Equal, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeBool),
+  ArithmeticCPUKernel);
+MS_REG_CPU_KERNEL(
+  Equal, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeBool),
+  ArithmeticCPUKernel);
+MS_REG_CPU_KERNEL(
+  Equal, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeBool),
+  ArithmeticCPUKernel);
+
+MS_REG_CPU_KERNEL(
+  NotEqual, KernelAttr().AddInputAttr(kNumberTypeBool).AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool),
+  ArithmeticCPUKernel);
+MS_REG_CPU_KERNEL(
+  NotEqual, KernelAttr().AddInputAttr(kNumberTypeInt8).AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeBool),
+  ArithmeticCPUKernel);
+MS_REG_CPU_KERNEL(
+  NotEqual, KernelAttr().AddInputAttr(kNumberTypeInt16).AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeBool),
+  ArithmeticCPUKernel);
+MS_REG_CPU_KERNEL(
+  NotEqual, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeBool),
+  ArithmeticCPUKernel);
+MS_REG_CPU_KERNEL(
+  NotEqual, KernelAttr().AddInputAttr(kNumberTypeUInt8).AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeBool),
+  ArithmeticCPUKernel);
+MS_REG_CPU_KERNEL(
+  NotEqual, KernelAttr().AddInputAttr(kNumberTypeUInt16).AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeBool),
+  ArithmeticCPUKernel);
+MS_REG_CPU_KERNEL(
+  NotEqual, KernelAttr().AddInputAttr(kNumberTypeUInt32).AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeBool),
+  ArithmeticCPUKernel);
+MS_REG_CPU_KERNEL(
+  NotEqual,
+  KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeBool),
+  ArithmeticCPUKernel);
+MS_REG_CPU_KERNEL(
+  NotEqual,
+  KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeBool),
+  ArithmeticCPUKernel);
+MS_REG_CPU_KERNEL(
+  NotEqual,
+  KernelAttr().AddInputAttr(kNumberTypeFloat64).AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeBool),
+  ArithmeticCPUKernel);
 }  // namespace kernel
 }  // namespace mindspore
 

mindspore/ccsrc/backend/kernel_compiler/cpu/arithmetic_self_cpu_kernel.cc

@@ -29,6 +29,19 @@ void Square(const T *in, T *out, size_t start, size_t end) {
   }
 }
 
+template <typename T>
+void Sign(const T *in, T *out, size_t start, size_t end) {
+  for (size_t i = start; i < end; i++) {
+    if (in[i] < 0) {
+      out[i] = -1;
+    } else if (in[i] > 0) {
+      out[i] = 1;
+    } else {
+      out[i] = 0;
+    }
+  }
+}
+
 template <typename T>
 void Neg(const T *in, T *out, size_t start, size_t end) {
   for (size_t i = start; i < end; i++) {
@@ -62,6 +75,8 @@ void ArithmeticSelfCPUKernel::InitKernel(const CNodePtr &kernel_node) {
     operate_type_ = ZEROSLIKE;
   } else if (kernel_name == prim::kPrimNeg->name()) {
     operate_type_ = NEG;
+  } else if (kernel_name == prim::kPrimSign->name()) {
+    operate_type_ = SIGN;
   }
   dtype_ = AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0);
 }
@@ -111,6 +126,8 @@ void ArithmeticSelfCPUKernel::LaunchKernel(const std::vector<AddressPtr> &inputs
       threads.emplace_back(std::thread(OnesLike<T>, input, output, start, end));
     } else if (operate_type_ == ZEROSLIKE) {
       threads.emplace_back(std::thread(ZerosLike<T>, input, output, start, end));
+    } else if (operate_type_ == SIGN) {
+      threads.emplace_back(std::thread(Sign<T>, input, output, start, end));
     }
     start += once_compute_size;
   }

mindspore/ccsrc/backend/kernel_compiler/cpu/arithmetic_self_cpu_kernel.h

@@ -54,6 +54,10 @@ MS_REG_CPU_KERNEL(OnesLike, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOut
                   ArithmeticSelfCPUKernel);
 MS_REG_CPU_KERNEL(OnesLike, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
                   ArithmeticSelfCPUKernel);
+MS_REG_CPU_KERNEL(Sign, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+                  ArithmeticSelfCPUKernel);
+MS_REG_CPU_KERNEL(Sign, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
+                  ArithmeticSelfCPUKernel);
 }  // namespace kernel
 }  // namespace mindspore
 

mindspore/ccsrc/backend/kernel_compiler/cpu/cpu_kernel.h

@@ -71,7 +71,10 @@ enum OperateType {
   SQRTGRAD,
   SIGMOIDGRAD,
   ONESLIKE,
-  ZEROSLIKE
+  ZEROSLIKE,
+  SIGN,
+  EQUAL,
+  NOTEQUAL,
 };
 
 class CPUKernel : public kernel::KernelMod {

mindspore/ccsrc/backend/kernel_compiler/cpu/equal_cpu_kernel.cc

@@ -1,99 +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 "backend/kernel_compiler/cpu/equal_cpu_kernel.h"
-#include "runtime/device/cpu/cpu_device_address.h"
-
-namespace mindspore {
-namespace kernel {
-void EqualCPUKernel::InitKernel(const CNodePtr &kernel_node) {
-  CheckParam(kernel_node);
-
-  dtype_ = AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0);
-  if (dtype_ != AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 1)) {
-    MS_LOG(EXCEPTION) << "Input0 and input1 must has the same data type";
-  }
-}
-
-bool EqualCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
-                            const std::vector<kernel::AddressPtr> & /*workspace*/,
-                            const std::vector<kernel::AddressPtr> &outputs) {
-  if (dtype_ == kNumberTypeBool) {
-    LaunchKernel<bool>(inputs, outputs);
-  } else if (dtype_ == kNumberTypeInt8) {
-    LaunchKernel<int8_t>(inputs, outputs);
-  } else if (dtype_ == kNumberTypeInt16) {
-    LaunchKernel<int16_t>(inputs, outputs);
-  } else if (dtype_ == kNumberTypeInt32 || dtype_ == kNumberTypeInt) {
-    LaunchKernel<int32_t>(inputs, outputs);
-  } else if (dtype_ == kNumberTypeInt64) {
-    LaunchKernel<int64_t>(inputs, outputs);
-  } else if (dtype_ == kNumberTypeUInt8) {
-    LaunchKernel<uint8_t>(inputs, outputs);
-  } else if (dtype_ == kNumberTypeUInt16) {
-    LaunchKernel<uint16_t>(inputs, outputs);
-  } else if (dtype_ == kNumberTypeUInt32 || dtype_ == kNumberTypeUInt) {
-    LaunchKernel<uint32_t>(inputs, outputs);
-  } else if (dtype_ == kNumberTypeUInt64) {
-    LaunchKernel<uint64_t>(inputs, outputs);
-  } else if (dtype_ == kNumberTypeFloat16) {
-    LaunchKernel<float16>(inputs, outputs);
-  } else if (dtype_ == kNumberTypeFloat32 || dtype_ == kNumberTypeFloat) {
-    LaunchKernel<float>(inputs, outputs);
-  } else if (dtype_ == kNumberTypeFloat64) {
-    LaunchKernel<double>(inputs, outputs);
-  } else {
-    MS_LOG(EXCEPTION) << "Only support bool, int, uint, float, but actual data type is " << TypeIdLabel(dtype_);
-  }
-  return true;
-}
-
-template <typename T>
-void EqualCPUKernel::LaunchKernel(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &outputs) {
-  T *left = reinterpret_cast<T *>(inputs[0]->addr);
-  T *right = reinterpret_cast<T *>(inputs[1]->addr);
-  bool *output = reinterpret_cast<bool *>(outputs[0]->addr);
-  size_t elem_num = inputs[0]->size / sizeof(T);
-  for (size_t i = 0; i < elem_num; i++) {
-    if (left[i] == right[i]) {
-      output[i] = true;
-    } else {
-      output[i] = false;
-    }
-  }
-}
-
-void EqualCPUKernel::CheckParam(const CNodePtr &kernel_node) {
-  size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
-  if (input_num != 2) {
-    MS_LOG(EXCEPTION) << "Input number is " << input_num << ", but EqualCPUKernel needs 2 inputs.";
-  }
-  size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
-  if (output_num != 1) {
-    MS_LOG(EXCEPTION) << "Output number is " << output_num << ", but EqualCPUKernel needs 1 output.";
-  }
-  auto input_shape0 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
-  auto input_shape1 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
-  if (input_shape0.size() != input_shape1.size()) {
-    MS_LOG(EXCEPTION) << "Input0 and Input1 must have the same shape";
-  }
-  for (size_t i = 0; i < input_shape0.size(); ++i) {
-    if (input_shape0[i] != input_shape1[i]) {
-      MS_LOG(EXCEPTION) << "Input0 and Input1 must have the same shape";
-    }
-  }
-}
-}  // namespace kernel
-}  // namespace mindspore

mindspore/ccsrc/backend/kernel_compiler/cpu/equal_cpu_kernel.h

@@ -1,75 +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.
- */
-#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_EQUAL_CPU_KERNEL_H_
-#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_EQUAL_CPU_KERNEL_H_
-#include <vector>
-#include <memory>
-#include "backend/kernel_compiler/cpu/cpu_kernel.h"
-#include "backend/kernel_compiler/cpu/cpu_kernel_factory.h"
-
-namespace mindspore {
-namespace kernel {
-class EqualCPUKernel : public CPUKernel {
- public:
-  EqualCPUKernel() = default;
-  ~EqualCPUKernel() override = default;
-
-  void InitKernel(const CNodePtr &kernel_node) override;
-
-  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
-              const std::vector<AddressPtr> &outputs) override;
-  template <typename T>
-  void LaunchKernel(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &outputs);
-
- private:
-  void CheckParam(const CNodePtr &kernel_node);
-  TypeId dtype_{kTypeUnknown};
-};
-
-MS_REG_CPU_KERNEL(
-  Equal, KernelAttr().AddInputAttr(kNumberTypeBool).AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool),
-  EqualCPUKernel);
-MS_REG_CPU_KERNEL(
-  Equal, KernelAttr().AddInputAttr(kNumberTypeInt8).AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeBool),
-  EqualCPUKernel);
-MS_REG_CPU_KERNEL(
-  Equal, KernelAttr().AddInputAttr(kNumberTypeInt16).AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeBool),
-  EqualCPUKernel);
-MS_REG_CPU_KERNEL(
-  Equal, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeBool),
-  EqualCPUKernel);
-MS_REG_CPU_KERNEL(
-  Equal, KernelAttr().AddInputAttr(kNumberTypeUInt8).AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeBool),
-  EqualCPUKernel);
-MS_REG_CPU_KERNEL(
-  Equal, KernelAttr().AddInputAttr(kNumberTypeUInt16).AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeBool),
-  EqualCPUKernel);
-MS_REG_CPU_KERNEL(
-  Equal, KernelAttr().AddInputAttr(kNumberTypeUInt32).AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeBool),
-  EqualCPUKernel);
-MS_REG_CPU_KERNEL(
-  Equal, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeBool),
-  EqualCPUKernel);
-MS_REG_CPU_KERNEL(
-  Equal, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeBool),
-  EqualCPUKernel);
-MS_REG_CPU_KERNEL(
-  Equal, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeBool),
-  EqualCPUKernel);
-}  // namespace kernel
-}  // namespace mindspore
-
-#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_EQUAL_CPU_KERNEL_H_

mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/pooling_avg_grad_cpu_kernel.cc

@@ -0,0 +1,89 @@
+/**
+ * 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 "backend/kernel_compiler/cpu/mkldnn/pooling_avg_grad_cpu_kernel.h"
+#include <string>
+#include <utility>
+#include <algorithm>
+#include "utils/ms_utils.h"
+#include "backend/kernel_compiler/cpu/mkldnn/mkl_kernel_engine.h"
+#include "runtime/device/cpu/cpu_device_address.h"
+
+namespace mindspore {
+namespace kernel {
+void AvgPoolingGradCPUKernel::InitKernel(const CNodePtr &kernel_node) {
+  MS_EXCEPTION_IF_NULL(kernel_node);
+  std::vector<size_t> src_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
+  std::vector<size_t> dst_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 1);
+  dnnl::memory::desc src_desc = GetDefaultMemDesc(src_shape);
+  dnnl::memory::desc dst_desc = GetDefaultMemDesc(dst_shape);
+  std::vector<int> origin_kernel_sizes;
+  std::vector<int> strides;
+  std::vector<int64_t> kernel_sizes_me = AnfAlgo::GetNodeAttr<std::vector<int64_t>>(kernel_node, KSIZE);
+  std::vector<int64_t> strides_me = AnfAlgo::GetNodeAttr<std::vector<int64_t>>(kernel_node, STRIDES);
+  (void)std::transform(kernel_sizes_me.begin(), kernel_sizes_me.end(), std::back_inserter(origin_kernel_sizes),
+                       [](const int64_t &value) { return static_cast<int>(value); });
+  (void)std::transform(strides_me.begin(), strides_me.end(), std::back_inserter(strides),
+                       [](const int64_t &value) { return static_cast<int>(value); });
+  if (origin_kernel_sizes.size() != 4 || strides.size() != 4) {
+    MS_LOG(EXCEPTION) << "Invalid kernel size " << origin_kernel_sizes.size() << " or stride size " << strides.size();
+  }
+  dnnl::memory::dims strides_dims{strides[2], strides[3]};
+  dnnl::memory::dims kernels_dims{origin_kernel_sizes[2], origin_kernel_sizes[3]};
+  const std::string pad_mode = AnfAlgo::GetNodeAttr<std::string>(kernel_node, PADDING);
+  std::vector<int> int_padding_l;
+  std::vector<int> int_padding_r;
+  std::vector<size_t> kernel_size({IntToSize(origin_kernel_sizes[2]), IntToSize(origin_kernel_sizes[3])});
+  GetPadding(kernel_node, pad_mode, src_shape, kernel_size, strides[3], &int_padding_l, &int_padding_r);
+  if (int_padding_l.size() != 2 || int_padding_r.size() != 2) {
+    MS_LOG(EXCEPTION) << "Pooling avg get padding failed";
+  }
+  dnnl::memory::dims padding_l{int_padding_l[0], int_padding_l[1]};
+  dnnl::memory::dims padding_r{int_padding_r[0], int_padding_r[1]};
+
+  // pooling_avg forward description
+  dnnl::pooling_forward::desc desc =
+    dnnl::pooling_forward::desc(dnnl::prop_kind::forward_training, dnnl::algorithm::pooling_avg, src_desc, dst_desc,
+                                strides_dims, kernels_dims, padding_l, padding_r);
+  auto prim_desc = dnnl::pooling_forward::primitive_desc(desc, MKLKernelEngine::Get().engine());
+
+  // pooling_avg backward description
+  dnnl::pooling_backward::desc backward_desc = dnnl::pooling_backward::desc(
+    dnnl::algorithm::pooling_avg, src_desc, dst_desc, strides_dims, kernels_dims, padding_l, padding_r);
+  auto backward_prim_desc =
+    dnnl::pooling_backward::primitive_desc(backward_desc, MKLKernelEngine::Get().engine(), prim_desc);
+
+  primitive_ = std::make_shared<dnnl::pooling_backward>(backward_prim_desc);
+  AddArgument(DNNL_ARG_SRC, src_desc);
+  AddArgument(DNNL_ARG_DST, dst_desc);
+  AddArgument(DNNL_ARG_DIFF_SRC, src_desc);
+  AddArgument(DNNL_ARG_DIFF_DST, dst_desc);
+}
+
+bool AvgPoolingGradCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
+                                     const std::vector<kernel::AddressPtr> & /*workspace*/,
+                                     const std::vector<kernel::AddressPtr> &outputs) {
+  if (inputs.size() < 3 || outputs.empty()) {
+    MS_LOG(EXCEPTION) << "Pooling avg grad error input output size!";
+  }
+  SetArgumentHandle(DNNL_ARG_SRC, inputs[0]->addr);
+  SetArgumentHandle(DNNL_ARG_DST, inputs[1]->addr);
+  SetArgumentHandle(DNNL_ARG_DIFF_DST, inputs[2]->addr);
+  SetArgumentHandle(DNNL_ARG_DIFF_SRC, outputs[0]->addr);
+  ExecutePrimitive();
+  return true;
+}
+}  // namespace kernel
+}  // namespace mindspore

mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/pooling_avg_grad_cpu_kernel.h

@@ -0,0 +1,51 @@
+/**
+ * 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_BACKEND_KERNEL_COMPILER_CPU_POOLING_AVG_GRAD_CPU_KERNEL_H_
+#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_POOLING_AVG_GRAD_CPU_KERNEL_H_
+
+#include <vector>
+#include <memory>
+#include <utility>
+#include "backend/kernel_compiler/cpu/mkldnn/mkl_cpu_kernel.h"
+
+namespace mindspore {
+namespace kernel {
+class AvgPoolingGradCPUKernel : public MKLCPUKernel {
+ public:
+  AvgPoolingGradCPUKernel() = default;
+  ~AvgPoolingGradCPUKernel() override = default;
+
+  void InitKernel(const CNodePtr &kernel_node) override;
+
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs) override;
+
+ private:
+  int stride_{0};
+  std::vector<size_t> kernel_size_;
+};
+
+MS_REG_CPU_KERNEL(AvgPoolGradCpu,
+                  KernelAttr()
+                    .AddInputAttr(kNumberTypeFloat32)
+                    .AddInputAttr(kNumberTypeFloat32)
+                    .AddInputAttr(kNumberTypeFloat32)
+                    .AddOutputAttr(kNumberTypeFloat32),
+                  AvgPoolingGradCPUKernel);
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_POOLING_AVG_GRAD_CPU_KERNEL_H_

mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/pooling_cpu_kernel.cc

@@ -54,6 +54,11 @@ void PoolingCPUKernel::InitKernel(const CNodePtr &kernel_node) {
   dnnl::pooling_forward::desc desc =
     dnnl::pooling_forward::desc(dnnl::prop_kind::forward_training, dnnl::algorithm::pooling_max, src_desc, dst_desc,
                                 strides_dims, kernels_dims, padding_l, padding_r);
+  std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node);
+  if (kernel_name == prim::kPrimAvgPool->name()) {
+    desc = dnnl::pooling_forward::desc(dnnl::prop_kind::forward_training, dnnl::algorithm::pooling_avg, src_desc,
+                                       dst_desc, strides_dims, kernels_dims, padding_l, padding_r);
+  }
   auto prim_desc = dnnl::pooling_forward::primitive_desc(desc, MKLKernelEngine::Get().engine());
   primitive_ = std::make_shared<dnnl::pooling_forward>(prim_desc);
   AddArgument(DNNL_ARG_SRC, src_desc);

mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/pooling_cpu_kernel.h

@@ -35,6 +35,8 @@ class PoolingCPUKernel : public MKLCPUKernel {
 
 MS_REG_CPU_KERNEL(MaxPool, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
                   PoolingCPUKernel);
+MS_REG_CPU_KERNEL(AvgPool, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+                  PoolingCPUKernel);
 }  // namespace kernel
 }  // namespace mindspore
 

mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/pooling_grad_cpu_kernel.cc → mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/pooling_max_grad_cpu_kernel.cc

@@ -13,7 +13,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "backend/kernel_compiler/cpu/mkldnn/pooling_grad_cpu_kernel.h"
+#include "backend/kernel_compiler/cpu/mkldnn/pooling_max_grad_cpu_kernel.h"
 #include <string>
 #include <utility>
 #include <algorithm>
@@ -23,7 +23,7 @@
 
 namespace mindspore {
 namespace kernel {
-void PoolingGradCPUKernel::InitKernel(const CNodePtr &kernel_node) {
+void MaxPoolingGradCPUKernel::InitKernel(const CNodePtr &kernel_node) {
   MS_EXCEPTION_IF_NULL(kernel_node);
   src_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
   dst_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 1);
@@ -45,9 +45,9 @@ void PoolingGradCPUKernel::InitKernel(const CNodePtr &kernel_node) {
   GetPadding(kernel_node, pad_mode, src_shape_, kernel_size_, stride_, &padding_l_, &padding_r);
 }
 
-void PoolingGradCPUKernel::RowPoolingGrad(const float *input, float *output, float diff,
-                                          const std::vector<std::pair<size_t, size_t>> &box,
-                                          std::vector<std::pair<size_t, float>> *row_max_pair) {
+void MaxPoolingGradCPUKernel::RowPoolingGrad(const float *input, float *output, float diff,
+                                             const std::vector<std::pair<size_t, size_t>> &box,
+                                             std::vector<std::pair<size_t, float>> *row_max_pair) {
   float max_value = 0;
   size_t max_index = box[1].second;
   size_t src_width = src_shape_[3];
@@ -74,7 +74,7 @@ void PoolingGradCPUKernel::RowPoolingGrad(const float *input, float *output, flo
   output[(*row_max_pair)[max_index].first] += diff;
 }
 
-void PoolingGradCPUKernel::ChannelPoolingGrad(const float *input, const float *diff, float *output) {
+void MaxPoolingGradCPUKernel::ChannelPoolingGrad(const float *input, const float *diff, float *output) {
   int src_width = SizeToInt(src_shape_[3]);
   int src_height = SizeToInt(src_shape_[2]);
   std::vector<std::pair<size_t, float>> row_max_pair(src_shape_[3]);
@@ -100,9 +100,9 @@ void PoolingGradCPUKernel::ChannelPoolingGrad(const float *input, const float *d
   }
 }
 
-bool PoolingGradCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
-                                  const std::vector<kernel::AddressPtr> & /*workspace*/,
-                                  const std::vector<kernel::AddressPtr> &outputs) {
+bool MaxPoolingGradCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
+                                     const std::vector<kernel::AddressPtr> & /*workspace*/,
+                                     const std::vector<kernel::AddressPtr> &outputs) {
   if (inputs.size() < 3 || outputs.empty()) {
     MS_LOG(EXCEPTION) << "pooling grad error input output size!";
   }

mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/pooling_grad_cpu_kernel.h → mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/pooling_max_grad_cpu_kernel.h

@@ -13,8 +13,8 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_POOLING_GRAD_CPU_KERNEL_H_
-#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_POOLING_GRAD_CPU_KERNEL_H_
+#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_POOLING_MAX_GRAD_CPU_KERNEL_H_
+#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_POOLING_MAX_GRAD_CPU_KERNEL_H_
 
 #include <vector>
 #include <memory>
@@ -23,10 +23,10 @@
 
 namespace mindspore {
 namespace kernel {
-class PoolingGradCPUKernel : public MKLCPUKernel {
+class MaxPoolingGradCPUKernel : public MKLCPUKernel {
  public:
-  PoolingGradCPUKernel() = default;
-  ~PoolingGradCPUKernel() override = default;
+  MaxPoolingGradCPUKernel() = default;
+  ~MaxPoolingGradCPUKernel() override = default;
 
   void InitKernel(const CNodePtr &kernel_node) override;
 
@@ -50,8 +50,8 @@ MS_REG_CPU_KERNEL(MaxPoolGrad,
                     .AddInputAttr(kNumberTypeFloat32)
                     .AddInputAttr(kNumberTypeFloat32)
                     .AddOutputAttr(kNumberTypeFloat32),
-                  PoolingGradCPUKernel);
+                  MaxPoolingGradCPUKernel);
 }  // namespace kernel
 }  // namespace mindspore
 
-#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_POOLING_GRAD_CPU_KERNEL_H_
+#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_POOLING_MAX_GRAD_CPU_KERNEL_H_

mindspore/ccsrc/backend/kernel_compiler/cpu/split_cpu_kernel.cc

@@ -0,0 +1,124 @@
+/**
+ * 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 "backend/kernel_compiler/cpu/split_cpu_kernel.h"
+#include "runtime/device/cpu/cpu_device_address.h"
+
+namespace mindspore {
+namespace kernel {
+void SplitCPUKernel::InitKernel(const CNodePtr &kernel_node) {
+  CheckParam(kernel_node);
+
+  axis_ = AnfAlgo::GetNodeAttr<int64_t>(kernel_node, AXIS);
+  auto output_1_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0);
+  if (axis_ < 0) {
+    axis_ = axis_ + SizeToLong(output_1_shape.size());
+  }
+  axis_ += 4 - SizeToLong(output_1_shape.size());
+
+  auto output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
+  for (size_t i = 0; i < output_num; i++) {
+    auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, i);
+    CPUKernelUtils::ExpandDimsTo4(&output_shape);
+    output_shape_list_.push_back(output_shape);
+  }
+
+  input_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+  CPUKernelUtils::ExpandDimsTo4(&input_shape_);
+
+  dtype_ = AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0);
+}
+
+bool SplitCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
+                            const std::vector<kernel::AddressPtr> & /*workspace*/,
+                            const std::vector<kernel::AddressPtr> &outputs) {
+  if (dtype_ == kNumberTypeInt32 || dtype_ == kNumberTypeInt) {
+    return LaunchKernel<int32_t>(inputs, outputs);
+  } else if (dtype_ == kNumberTypeInt64) {
+    return LaunchKernel<int64_t>(inputs, outputs);
+  } else if (dtype_ == kNumberTypeFloat32 || dtype_ == kNumberTypeFloat) {
+    return LaunchKernel<float>(inputs, outputs);
+  } else if (dtype_ == kNumberTypeFloat64) {
+    return LaunchKernel<double>(inputs, outputs);
+  } else {
+    MS_LOG(EXCEPTION) << "Only support int, float, but actual data type is " << TypeIdLabel(dtype_);
+  }
+}
+
+template <typename T>
+bool SplitCPUKernel::LaunchKernel(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &outputs) {
+  auto input_addr = reinterpret_cast<T *>(inputs[0]->addr);
+  auto buff_size = inputs[0]->size;
+  size_t dim0 = input_shape_[0];
+  size_t dim1 = input_shape_[1];
+  size_t dim2 = input_shape_[2];
+
+  if (axis_ == 3) {
+    for (size_t i = 0; i < dim0; ++i) {
+      for (size_t j = 0; j < dim1; ++j) {
+        for (size_t k = 0; k < dim2; ++k) {
+          CopyDataToOutput(outputs, i, j, k, &input_addr, &buff_size);
+        }
+      }
+    }
+  } else if (axis_ == 2) {
+    for (size_t i = 0; i < dim0; ++i) {
+      for (size_t j = 0; j < dim1; ++j) {
+        CopyDataToOutput(outputs, i, j, 0, &input_addr, &buff_size);
+      }
+    }
+  } else if (axis_ == 1) {
+    for (size_t i = 0; i < dim0; ++i) {
+      CopyDataToOutput(outputs, i, 0, 0, &input_addr, &buff_size);
+    }
+  } else if (axis_ == 0) {
+    CopyDataToOutput(outputs, 0, 0, 0, &input_addr, &buff_size);
+  }
+  return true;
+}
+
+template <typename T>
+void SplitCPUKernel::CopyDataToOutput(const std::vector<kernel::AddressPtr> &outputs, size_t dim0, size_t dim1,
+                                      size_t dim2, T **input_addr, size_t *buff_size) {
+  for (size_t i = 0; i < output_shape_list_.size(); ++i) {
+    auto output_i_shape = output_shape_list_[i];
+    auto output_i_addr = reinterpret_cast<float *>(outputs[i]->addr);
+
+    size_t num = CPUKernelUtils::GetElementNumOnAxis(output_i_shape, axis_);
+    num *= output_i_shape[axis_];
+    auto pos = CPUKernelUtils::CalcOffset(output_i_shape, dim0, dim1, dim2, 0);
+    auto ret = memcpy_s(output_i_addr + pos, *buff_size, *input_addr, num * sizeof(T));
+    if (ret != EOK) {
+      MS_LOG(EXCEPTION) << "memcpy failed.";
+    }
+    *input_addr += num;
+    *buff_size -= num * sizeof(T);
+  }
+}
+
+void SplitCPUKernel::CheckParam(const CNodePtr &kernel_node) {
+  auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0);
+  if (output_shape.size() > 4) {
+    MS_LOG(EXCEPTION) << "Output dims is " << output_shape.size() << ", but SplitCPUKernel only support 4d or lower.";
+  }
+
+  size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+  if (input_num != 1) {
+    MS_LOG(EXCEPTION) << "Input number is " << input_num << ", but SplitCPUKernel needs 1 input.";
+  }
+}
+}  // namespace kernel
+}  // namespace mindspore

mindspore/ccsrc/backend/kernel_compiler/cpu/split_cpu_kernel.h

@@ -0,0 +1,58 @@
+/**
+ * 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_BACKEND_KERNEL_COMPILER_CPU_SPLIT_CPU_KERNEL_H_
+#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_SPLIT_CPU_KERNEL_H_
+#include <vector>
+#include <memory>
+#include "backend/kernel_compiler/cpu/cpu_kernel.h"
+#include "backend/kernel_compiler/cpu/cpu_kernel_factory.h"
+
+namespace mindspore {
+namespace kernel {
+class SplitCPUKernel : public CPUKernel {
+ public:
+  SplitCPUKernel() : axis_(0) {}
+  ~SplitCPUKernel() override = default;
+
+  void InitKernel(const CNodePtr &kernel_node) override;
+
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs) override;
+
+  template <typename T>
+  bool LaunchKernel(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &outputs);
+
+ private:
+  static void CheckParam(const CNodePtr &kernel_node);
+  template <typename T>
+  void CopyDataToOutput(const std::vector<kernel::AddressPtr> &inputs, size_t dim0, size_t dim1, size_t dim2,
+                        T **output_addr, size_t *buff_size);
+  int64_t axis_;
+  std::vector<std::vector<size_t>> output_shape_list_;
+  std::vector<size_t> input_shape_;
+  TypeId dtype_{kTypeUnknown};
+};
+
+MS_REG_CPU_KERNEL(Split,
+                  KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+                  SplitCPUKernel);
+MS_REG_CPU_KERNEL(Split,
+                  KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
+                  SplitCPUKernel);
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_SPLIT_CPU_KERNEL_H_

mindspore/core/base/core_ops.h

@@ -141,6 +141,7 @@ inline const PrimitivePtr kPrimApplyCenteredRMSProp = std::make_shared<Primitive
 inline const PrimitivePtr kPrimAvgPool = std::make_shared<Primitive>("AvgPool");
 inline const PrimitivePtr kPrimAvgPoolGrad = std::make_shared<Primitive>("AvgPoolGrad");
 inline const PrimitivePtr kPrimAvgPoolGradVm = std::make_shared<Primitive>("AvgPoolGradVm");
+inline const PrimitivePtr kPrimAvgPoolGradCpu = std::make_shared<Primitive>("AvgPoolGradCpu");
 inline const PrimitivePtr kPrimFusedSparseAdam = std::make_shared<Primitive>("FusedSparseAdam");
 inline const PrimitivePtr kPrimFusedBatchNorm = std::make_shared<Primitive>("FusedBatchNorm");
 inline const PrimitivePtr kPrimFusedBatchNormEx = std::make_shared<Primitive>("FusedBatchNormEx");
@@ -263,6 +264,7 @@ inline const PrimitivePtr kPrimLog = std::make_shared<Primitive>("Log");
 inline const PrimitivePtr kPrimRsqrt = std::make_shared<Primitive>("Rsqrt");
 inline const PrimitivePtr kPrimSplitV = std::make_shared<Primitive>("SplitV");
 inline const PrimitivePtr kPrimLinSpace = std::make_shared<Primitive>("LinSpace");
+inline const PrimitivePtr kPrimSign = std::make_shared<Primitive>("Sign");
 
 // Statements
 inline const PrimitivePtr kPrimReturn = std::make_shared<Primitive>("return");

mindspore/ops/_grad/grad_nn_ops.py

@@ -338,6 +338,19 @@ def get_bprop_avg_pool_grad(self):
 
         bprop_fn = bprop_gpu
 
+    elif self.target == "CPU":
+        avgpool_grad_cpu = G.AvgPoolGradCpu(
+            ksize=self.ksize,
+            strides=self.strides,
+            padding=self.padding,
+            data_format=self.format)
+
+        def bprop_cpu(x, out, dout):
+            dx = avgpool_grad_cpu(x, out, dout)
+            return (dx,)
+
+        bprop_fn = bprop_cpu
+
     elif self.target == "GE":
         avgpool_grad_ge = G.AvgPoolGrad(
                         ksize=self.ksize,

mindspore/ops/operations/_grad_ops.py

@@ -885,6 +885,20 @@ class AvgPoolGradGpu(_PoolGrad):
         return x1_dtype
 
 
+class AvgPoolGradCpu(_PoolGrad):
+    """Gradients of the avg pool operation for cpu."""
+
+    @prim_attr_register
+    def __init__(self, ksize=1, strides=1, padding="VALID", data_format="NCHW"):
+        super(AvgPoolGradCpu, self).__init__(ksize, strides, padding, data_format)
+
+    def infer_shape(self, x1_shape, x2_shape, grad_shape):
+        return x1_shape
+
+    def infer_dtype(self, x1_dtype, x2_dtype, grad_dtype):
+        return x1_dtype
+
+
 class MaxPoolGrad(_PoolGrad):
     """Performs gradients of the max pool operation."""
 

mindspore/ops/operations/array_ops.py

@@ -951,7 +951,7 @@ class Split(PrimitiveWithCheck):
         :math:`(y_1, y_2, ..., y_S)`.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> split = ops.Split(1, 2)

mindspore/ops/operations/math_ops.py

@@ -2519,7 +2519,7 @@ class Equal(_LogicBinaryOp):
         Tensor, the shape is the same as the one after broadcasting,and the data type is bool.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.float32)
@@ -2656,7 +2656,7 @@ class NotEqual(_LogicBinaryOp):
         Tensor, the shape is the same as the one after broadcasting,and the data type is bool.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.float32)
@@ -3459,7 +3459,7 @@ class Sign(PrimitiveWithInfer):
         Tensor, has the same shape and type as the `input_x`.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``CPU``
 
     Examples:
          >>> input_x = Tensor(np.array([[2.0, 0.0, -1.0]]), mindspore.float32)

mindspore/ops/operations/nn_ops.py

@@ -1639,7 +1639,7 @@ class AvgPool(_Pool):
         Tensor, with shape :math:`(N, C_{out}, H_{out}, W_{out})`.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> import mindspore
@@ -1672,6 +1672,8 @@ class AvgPool(_Pool):
     def __init__(self, ksize=1, strides=1, padding="valid", data_format="NCHW"):
         if context.get_context("device_target") == "GPU":
             self.target = "GPU"
+        elif context.get_context("device_target") == "CPU":
+            self.target = "CPU"
         elif context.get_context("enable_ge"):
             self.target = "GE"
         else:

tests/st/ops/cpu/test_avgpool_grad_op.py

@@ -0,0 +1,51 @@
+# 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 pytest
+
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.common.api import ms_function
+from mindspore.ops.composite import GradOperation
+
+context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
+
+
+class Grad(nn.Cell):
+    def __init__(self, network):
+        super(Grad, self).__init__()
+        self.grad = GradOperation(get_all=True, sens_param=True)
+        self.network = network
+
+    @ms_function
+    def construct(self, input_, output_grad):
+        return self.grad(self.network)(input_, output_grad)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_net():
+    x = np.arange(1 * 1 * 6 * 6).reshape((1, 1, 6, 6)).astype(np.float32)
+    net = nn.AvgPool2d(kernel_size=3, stride=2, pad_mode='valid')
+    out = net(Tensor(x))
+
+    out_shape = out.asnumpy().shape
+    sens = np.arange(int(np.prod(out_shape))).reshape(out_shape).astype(np.float32)
+    backword_net = Grad(net)
+    output = backword_net(Tensor(x), Tensor(sens))
+    print(len(output))
+    print(output[0].asnumpy())

tests/st/ops/cpu/test_avgpool_op.py

@@ -0,0 +1,94 @@
+# 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 pytest
+
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+
+context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_avgpool_k2s1pv():
+    x = np.arange(1 * 1 * 6 * 6).reshape((1, 1, 6, 6)).astype(np.float32)
+    net = nn.AvgPool2d(kernel_size=2, stride=1, pad_mode='valid')
+    out = net(Tensor(x))
+    print(out)
+    expect_result = np.array(
+        [[[[3.5, 4.5, 5.5, 6.5, 7.5],
+           [9.5, 10.5, 11.5, 12.5, 13.5],
+           [15.5, 16.5, 17.5, 18.5, 19.5],
+           [21.5, 22.5, 23.5, 24.5, 25.5],
+           [27.5, 28.5, 29.5, 30.5, 31.5]]]]
+    )
+    assert np.allclose(out.asnumpy(), expect_result)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_avgpool_k2s2pv():
+    x = np.arange(1 * 1 * 6 * 6).reshape((1, 1, 6, 6)).astype(np.float32)
+    net = nn.AvgPool2d(kernel_size=2, stride=2, pad_mode='valid')
+    out = net(Tensor(x))
+    print(out)
+    expect_result = np.array(
+        [[[[3.5, 5.5, 7.5],
+           [15.5, 17.5, 19.5],
+           [27.5, 29.5, 31.5]]]]
+    )
+    assert np.allclose(out.asnumpy(), expect_result)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_avgpool_k3s2pv():
+    x = np.arange(1 * 1 * 6 * 6).reshape((1, 1, 6, 6)).astype(np.float32)
+    net = nn.AvgPool2d(kernel_size=3, stride=2, pad_mode='valid')
+    out = net(Tensor(x))
+    print(out)
+    expect_result = np.array(
+        [[[[7., 9.],
+           [19., 21.]]]]
+    )
+    assert np.allclose(out.asnumpy(), expect_result)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_avgpool_k3s2ps():
+    x = np.arange(1 * 1 * 6 * 6).reshape((1, 1, 6, 6)).astype(np.float32)
+    net = nn.AvgPool2d(kernel_size=3, stride=2, pad_mode='same')
+    out = net(Tensor(x))
+    print(out)
+    expect_result = np.array(
+        [[[[7., 9., 10.5],
+           [19., 21., 22.5],
+           [28., 30., 31.5]]]]
+    )
+    assert np.allclose(out.asnumpy(), expect_result)
+
+
+if __name__ == '__main__':
+    test_avgpool_k2s1pv()
+    test_avgpool_k2s2pv()
+    test_avgpool_k3s2pv()
+    test_avgpool_k3s2ps()

tests/st/ops/cpu/test_notequal_op.py

@@ -0,0 +1,68 @@
+# 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 pytest
+
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+
+context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
+
+
+class OpNetWrapper(nn.Cell):
+    def __init__(self, op):
+        super(OpNetWrapper, self).__init__()
+        self.op = op
+
+    def construct(self, *inputs):
+        return self.op(*inputs)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_notequal_int():
+    op = P.NotEqual()
+    op_wrapper = OpNetWrapper(op)
+
+    input_x = Tensor(np.array([1, 2, 3]).astype(np.int32))
+    input_y = Tensor(np.array([11, 2, 13]).astype(np.int32))
+    outputs = op_wrapper(input_x, input_y)
+
+    print(outputs)
+    assert np.allclose(outputs.asnumpy(), (True, False, True))
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_notequal_float():
+    op = P.NotEqual()
+    op_wrapper = OpNetWrapper(op)
+
+    input_x = Tensor(np.array([1, 2, 3]).astype(np.float32))
+    input_y = Tensor(np.array([-1, 0, 3]).astype(np.float32))
+    outputs = op_wrapper(input_x, input_y)
+
+    print(outputs)
+    assert np.allclose(outputs.asnumpy(), (True, True, False))
+
+
+if __name__ == '__main__':
+    test_notequal_int()
+    test_notequal_float()

tests/st/ops/cpu/test_sign_op.py

@@ -0,0 +1,66 @@
+# 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 pytest
+
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+
+context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
+
+
+class OpNetWrapper(nn.Cell):
+    def __init__(self, op):
+        super(OpNetWrapper, self).__init__()
+        self.op = op
+
+    def construct(self, *inputs):
+        return self.op(*inputs)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_sign_float32():
+    op = P.Sign()
+    op_wrapper = OpNetWrapper(op)
+
+    input_x = Tensor(np.array([[2.0, 0.0, -1.0]]).astype(np.float32))
+    outputs = op_wrapper(input_x)
+
+    print(outputs)
+    assert np.allclose(outputs.asnumpy(), [[1., 0., -1.]])
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_sign_int32():
+    op = P.Sign()
+    op_wrapper = OpNetWrapper(op)
+
+    input_x = Tensor(np.array([[20, 0, -10]]).astype(np.int32))
+    outputs = op_wrapper(input_x)
+
+    print(outputs)
+    assert np.allclose(outputs.asnumpy(), [[1, 0, -1]])
+
+
+if __name__ == '__main__':
+    test_sign_float32()
+    test_sign_int32()

tests/st/ops/cpu/test_split_op.py

@@ -0,0 +1,86 @@
+# 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 pytest
+
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+
+context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
+
+
+class OpNetWrapper(nn.Cell):
+    def __init__(self, op):
+        super(OpNetWrapper, self).__init__()
+        self.op = op
+
+    def construct(self, *inputs):
+        return self.op(*inputs)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_out1_axis0():
+    op = P.Split(0, 1)
+    op_wrapper = OpNetWrapper(op)
+
+    input_x = Tensor(np.arange(24).astype(np.int32).reshape((2, 2, 6)))
+    outputs = op_wrapper(input_x)
+
+    print(outputs)
+    assert outputs[0].shape == (2, 2, 6)
+    assert np.allclose(outputs[0].asnumpy()[0, 0, :], [0, 1, 2, 3, 4, 5])
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_out2_axis2():
+    op = P.Split(2, 2)
+    op_wrapper = OpNetWrapper(op)
+
+    input_x = Tensor(np.arange(24).astype(np.int32).reshape((2, 2, 6)))
+    outputs = op_wrapper(input_x)
+
+    print(outputs)
+    assert outputs[0].shape == (2, 2, 3)
+    assert outputs[1].shape == (2, 2, 3)
+    assert np.allclose(outputs[0].asnumpy()[0, 0, :], [0, 1, 2])
+    assert np.allclose(outputs[1].asnumpy()[0, 0, :], [3, 4, 5])
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_out2_axis1neg():
+    op = P.Split(-1, 2)
+    op_wrapper = OpNetWrapper(op)
+
+    input_x = Tensor(np.arange(24).astype(np.float32).reshape((2, 2, 6)))
+    outputs = op_wrapper(input_x)
+
+    print(outputs)
+    assert np.allclose(outputs[0].asnumpy()[0, :, :], [[0., 1., 2.], [6., 7., 8.]])
+    assert np.allclose(outputs[1].asnumpy()[0, :, :], [[3., 4., 5.], [9., 10., 11.]])
+
+
+if __name__ == '__main__':
+    test_out1_axis0()
+    test_out2_axis2()
+    test_out2_axis1neg()