!44428 refactor ScatterAddWithAxis and SearchSorted oprerators kernelmod.

Merge pull request !44428 from yangshuo/br_scatter_add_with_axis

mindspore/ccsrc/plugin/device/cpu/kernel/scatter_add_with_axis_cpu_kernel.cc

@@ -20,6 +20,7 @@
 #include <complex>
 
 #include "kernel/common_utils.h"
+#include "mindspore/core/ops/scatter_add_with_axis.h"
 #include "plugin/device/cpu/hal/device/cpu_device_address.h"
 
 namespace {
@@ -46,47 +47,68 @@ namespace {
     .AddOutputAttr(kNumberType##t4)
 const int32_t kInputNum = 3;
 const int32_t kOutputNum = 1;
-const uint32_t kInputIndex2 = 2;
 const int32_t KSplitSize = 64 * 1024;
 }  // namespace
 
-void ScatterAddWithAxisCpuKernelMod::InitKernel(const CNodePtr &kernel_node) {
-  MS_EXCEPTION_IF_NULL(kernel_node);
-  kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node);
-  x_type_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0);
-  indices_type_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 1);
-  if (indices_type_ != kNumberTypeInt32 && indices_type_ != kNumberTypeInt64) {
-    MS_EXCEPTION(ValueError) << "For '" << kernel_name_ << "', the dtype of 'indices' must be int32 or int64, but got "
-                             << indices_type_;
+bool ScatterAddWithAxisCpuKernelMod::Init(const BaseOperatorPtr &base_operator,
+                                          const std::vector<KernelTensorPtr> &inputs,
+                                          const std::vector<KernelTensorPtr> &outputs) {
+  MS_ERROR_IF_NULL(base_operator);
+  kernel_name_ = base_operator->name();
+  CHECK_KERNEL_INPUTS_NUM(inputs.size(), kInputNum, kernel_name_);
+  CHECK_KERNEL_OUTPUTS_NUM(outputs.size(), kOutputNum, kernel_name_);
+  auto op_prim = std::dynamic_pointer_cast<ops::ScatterAddWithAxis>(base_operator);
+  MS_ERROR_IF_NULL(op_prim);
+  axis_ = op_prim->get_axis();
+  auto kernel_attr = GetKernelAttrFromTensors(inputs, outputs);
+  auto is_match = MatchKernelAttr(kernel_attr, GetOpSupport());
+  if (!is_match.first) {
+    MS_LOG(ERROR) << "For '" << kernel_name_ << "', it does not support this kernel data type: " << kernel_attr;
+    return false;
   }
+  return true;
+}
 
-  // check parameters basic attribution are valid
-  x_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
-  indices_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 1);
-  updates_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, kInputIndex2);
-  axis_ = common::AnfAlgo::GetNodeAttr<int64_t>(kernel_node, "axis");
+int ScatterAddWithAxisCpuKernelMod::Resize(const BaseOperatorPtr &base_operator,
+                                           const std::vector<KernelTensorPtr> &inputs,
+                                           const std::vector<KernelTensorPtr> &outputs,
+                                           const std::map<uint32_t, tensor::TensorPtr> &) {
+  auto ret = KernelMod::Resize(base_operator, inputs, outputs);
+  if (ret != KRET_OK) {
+    return ret;
+  }
+  x_type_ = inputs[kIndex0]->GetDtype();
+  indices_type_ = inputs[kIndex1]->GetDtype();
+  x_shape_ = inputs[kIndex0]->GetDeviceShapeAdaptively();
+  indices_shape_ = inputs[kIndex1]->GetDeviceShapeAdaptively();
+  updates_shape_ = inputs[kIndex2]->GetDeviceShapeAdaptively();
 
   // Get and check 3 input dim info
   int64_t value_dim_num_x1 = static_cast<int64_t>(x_shape_.size());
   int64_t value_dim_num_x2 = static_cast<int64_t>(indices_shape_.size());
   int64_t value_dim_num_x3 = static_cast<int64_t>(updates_shape_.size());
   if (value_dim_num_x1 != value_dim_num_x2 || value_dim_num_x2 != value_dim_num_x3) {
-    MS_EXCEPTION(ValueError) << "For '" << kernel_name_ << "', the dim values of three inputs must be same, but got "
-                             << "data: " << value_dim_num_x1 << ", indices: " << value_dim_num_x2
-                             << ", update: " << value_dim_num_x3;
+    MS_LOG(ERROR) << "For '" << kernel_name_ << "', the dim values of three inputs must be same, but got "
+                  << "data: " << value_dim_num_x1 << ", indices: " << value_dim_num_x2
+                  << ", update: " << value_dim_num_x3;
+    return KRET_RESIZE_FAILED;
   }
   if (axis_ < value_dim_num_x1 * -1 || axis_ >= value_dim_num_x1) {
-    MS_EXCEPTION(ValueError) << "For '" << kernel_name_ << "', the value of axis is out of range!";
+    MS_LOG(ERROR) << "For '" << kernel_name_ << "', the value of axis is out of range!";
+    return KRET_RESIZE_FAILED;
   }
 
   int64_t sub_data_fix = 1;
   int64_t sub_index_fix = 1;
+  data_dim_vec_.clear();
+  index_dim_vec_.clear();
   for (int64_t i = value_dim_num_x2 - 1; i >= 0; --i) {
     size_t j = static_cast<size_t>(i);
     if (x_shape_[j] < indices_shape_[j] || indices_shape_[j] != updates_shape_[j] || updates_shape_[j] <= 0) {
-      MS_EXCEPTION(ValueError) << "For '" << kernel_name_ << "', the " << j << " dimension verification failed: "
-                               << "input0[" << x_shape_[j] << "], input1[" << indices_shape_[j] << "], input2["
-                               << updates_shape_[j] << "]";
+      MS_LOG(ERROR) << "For '" << kernel_name_ << "', the " << j << " dimension verification failed: "
+                    << "input0[" << x_shape_[j] << "], input1[" << indices_shape_[j] << "], input2["
+                    << updates_shape_[j] << "]";
+      return KRET_RESIZE_FAILED;
     }
     if (i > 0) {
       sub_data_fix *= x_shape_[j];
@@ -95,6 +117,7 @@ void ScatterAddWithAxisCpuKernelMod::InitKernel(const CNodePtr &kernel_node) {
       index_dim_vec_.push_back(sub_index_fix);
     }
   }
+  return KRET_OK;
 }
 
 bool ScatterAddWithAxisCpuKernelMod::Launch(const std::vector<AddressPtr> &inputs,

mindspore/ccsrc/plugin/device/cpu/kernel/scatter_add_with_axis_cpu_kernel.h

@@ -17,6 +17,7 @@
 #ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_SCATTER_ADD_WITH_AXIS_CPU_KERNEL_H_
 #define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_SCATTER_ADD_WITH_AXIS_CPU_KERNEL_H_
 
+#include <map>
 #include <memory>
 #include <string>
 #include <vector>
@@ -26,12 +27,16 @@
 
 namespace mindspore {
 namespace kernel {
-class ScatterAddWithAxisCpuKernelMod : public DeprecatedNativeCpuKernelMod {
+class ScatterAddWithAxisCpuKernelMod : public NativeCpuKernelMod {
  public:
   ScatterAddWithAxisCpuKernelMod() = default;
   ~ScatterAddWithAxisCpuKernelMod() override = default;
 
-  void InitKernel(const CNodePtr &kernel_node) override;
+  bool Init(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs,
+            const std::vector<KernelTensorPtr> &outputs) override;
+
+  int Resize(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs,
+             const std::vector<KernelTensorPtr> &outputs, const std::map<uint32_t, tensor::TensorPtr> &) override;
 
   bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
               const std::vector<AddressPtr> &outputs) override;

mindspore/ccsrc/plugin/device/cpu/kernel/searchsorted_cpu_kernel.cc

@@ -21,6 +21,7 @@
 #include <functional>
 #include <algorithm>
 #include <utility>
+#include "mindspore/core/ops/search_sorted.h"
 
 namespace mindspore {
 namespace kernel {
@@ -29,20 +30,35 @@ constexpr size_t kSearchSortedInputsNum = 2;
 constexpr size_t kSearchSortedOutputsNum = 1;
 }  // namespace
 
-void SearchSortedCpuKernelMod::InitKernel(const CNodePtr &kernel_node) {
-  MS_EXCEPTION_IF_NULL(kernel_node);
-  kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node);
-  right_ = common::AnfAlgo::GetNodeAttr<bool>(kernel_node, "right");
-  sequence_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
-  values_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 1);
-  search_len = LongToSize(sequence_shape_.back());
-
-  auto kernel_attr = GetKernelAttrFromNode(kernel_node);
+bool SearchSortedCpuKernelMod::Init(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs,
+                                    const std::vector<KernelTensorPtr> &outputs) {
+  MS_ERROR_IF_NULL(base_operator);
+  kernel_name_ = base_operator->name();
+  CHECK_KERNEL_INPUTS_NUM(inputs.size(), kSearchSortedInputsNum, kernel_name_);
+  CHECK_KERNEL_OUTPUTS_NUM(outputs.size(), kSearchSortedOutputsNum, kernel_name_);
+  auto op_prim = std::dynamic_pointer_cast<ops::SearchSorted>(base_operator);
+  right_ = op_prim->get_right();
+  auto kernel_attr = GetKernelAttrFromTensors(inputs, outputs);
   auto [is_match, index] = MatchKernelAttr(kernel_attr, GetOpSupport());
   if (!is_match) {
-    MS_LOG(EXCEPTION) << "SearchSorted does not support this kernel data type: " << kernel_attr;
+    MS_LOG(ERROR) << "SearchSorted does not support this kernel data type: " << kernel_attr;
+    return true;
   }
   kernel_func_ = func_list_[index].second;
+  return true;
+}
+
+int SearchSortedCpuKernelMod::Resize(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs,
+                                     const std::vector<KernelTensorPtr> &outputs,
+                                     const std::map<uint32_t, tensor::TensorPtr> &) {
+  auto ret = KernelMod::Resize(base_operator, inputs, outputs);
+  if (ret != KRET_OK) {
+    return ret;
+  }
+  sequence_shape_ = inputs[kIndex0]->GetDeviceShapeAdaptively();
+  values_shape_ = inputs[kIndex1]->GetDeviceShapeAdaptively();
+  search_len_ = LongToSize(sequence_shape_.back());
+  return KRET_OK;
 }
 
 template <typename S>
@@ -71,9 +87,9 @@ bool SearchSortedCpuKernelMod::LaunchKernel(const std::vector<kernel::AddressPtr
 
   auto task = [this, &sequence, &values, &output, seq_dim, search_repeat](size_t start, size_t end) {
     for (size_t i = start; i < end; i++) {
-      auto seq_start = (seq_dim == 1) ? sequence : sequence + (i / search_repeat) * search_len;
-      auto result = right_ ? std::upper_bound(seq_start, seq_start + search_len, values[i]) - seq_start
-                           : CustomizedLowerBound<S>(seq_start, seq_start + search_len, values[i]) - seq_start;
+      auto seq_start = (seq_dim == 1) ? sequence : sequence + (i / search_repeat) * search_len_;
+      auto result = right_ ? std::upper_bound(seq_start, seq_start + search_len_, values[i]) - seq_start
+                           : CustomizedLowerBound<S>(seq_start, seq_start + search_len_, values[i]) - seq_start;
       output[i] = static_cast<T>(result);
     }
   };
@@ -97,10 +113,10 @@ void SearchSortedCpuKernelMod::CheckParam(const std::vector<AddressPtr> &inputs,
   int list_count = accumulate(sequence_shape_.begin(), sequence_shape_.end() - 1, 1, std::multiplies<int>());
   auto task = [this, &sequence](size_t start, size_t end) {
     for (size_t i = start; i < end; i++) {
-      for (size_t j = 0; j < search_len - 1; j++) {
-        if (sequence[i * search_len + j] > sequence[i * search_len + j + 1]) {
+      for (size_t j = 0; j < search_len_ - 1; j++) {
+        if (sequence[i * search_len_ + j] > sequence[i * search_len_ + j + 1]) {
           MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the input sequence must be forward sequence. But got "
-                            << sequence[i * search_len + j] << '>' << sequence[i * search_len + j + 1];
+                            << sequence[i * search_len_ + j] << '>' << sequence[i * search_len_ + j + 1];
         }
       }
     }

mindspore/ccsrc/plugin/device/cpu/kernel/searchsorted_cpu_kernel.h

@@ -17,6 +17,7 @@
 #ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_SEARCHSORTED_CPU_KERNEL_H_
 #define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_SEARCHSORTED_CPU_KERNEL_H_
 
+#include <map>
 #include <vector>
 #include <utility>
 #include "plugin/device/cpu/kernel/cpu_kernel.h"
@@ -24,12 +25,16 @@
 
 namespace mindspore {
 namespace kernel {
-class SearchSortedCpuKernelMod : public DeprecatedNativeCpuKernelMod {
+class SearchSortedCpuKernelMod : public NativeCpuKernelMod {
  public:
   SearchSortedCpuKernelMod() = default;
   ~SearchSortedCpuKernelMod() override = default;
 
-  void InitKernel(const CNodePtr &kernel_node) override;
+  bool Init(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs,
+            const std::vector<KernelTensorPtr> &outputs) override;
+
+  int Resize(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs,
+             const std::vector<KernelTensorPtr> &outputs, const std::map<uint32_t, tensor::TensorPtr> &) override;
 
   bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
               const std::vector<AddressPtr> &outputs) override {
@@ -51,7 +56,7 @@ class SearchSortedCpuKernelMod : public DeprecatedNativeCpuKernelMod {
   SearchSortedFunc kernel_func_;
 
   bool right_{false};
-  size_t search_len{0};
+  size_t search_len_{0};
   std::vector<int64_t> sequence_shape_;
   std::vector<int64_t> values_shape_;
   std::vector<int64_t> output_shape_;

mindspore/core/ops/search_sorted.cc

@@ -0,0 +1,30 @@
+/**
+ * Copyright 2022 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 "ops/search_sorted.h"
+#include <string>
+#include "mindapi/src/helper.h"
+#include "ops/op_utils.h"
+
+namespace mindspore {
+namespace ops {
+constexpr auto kNameRight = "right";
+void SearchSorted::set_right(const bool right) { (void)AddAttr(kNameRight, api::MakeValue(right)); }
+bool SearchSorted::get_right() const { return GetValue<bool>(GetAttr(kNameRight)); }
+MIND_API_OPERATOR_IMPL(SearchSorted, BaseOperator);
+REGISTER_PRIMITIVE_C(kSearchSorted, SearchSorted);
+}  // namespace ops
+}  // namespace mindspore

mindspore/core/ops/search_sorted.h

@@ -0,0 +1,44 @@
+/**
+ * Copyright 2022 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_CORE_OPS_SEARCH_SORTED_H_
+#define MINDSPORE_CORE_OPS_SEARCH_SORTED_H_
+#include <memory>
+#include <vector>
+
+#include "mindapi/base/types.h"
+#include "ops/base_operator.h"
+
+namespace mindspore {
+namespace ops {
+constexpr auto kSearchSorted = "SearchSorted";
+/// \brief Updates tensor values by using input indices and value.
+/// Refer to Python API @ref mindspore.ops.SearchSorted for more details.
+class MIND_API SearchSorted : public BaseOperator {
+ public:
+  MIND_API_BASE_MEMBER(SearchSorted);
+  /// \brief Constructor.
+  SearchSorted() : BaseOperator(kSearchSorted) { InitIOName({"sequence", "values"}, {"positions"}); }
+
+  /// \brief Set right.
+  void set_right(const bool right);
+  /// \brief Get right.
+  bool get_right() const;
+};
+}  // namespace ops
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CORE_OPS_SEARCH_SORTED_H_