!1079 Convert AiCpu kernel when aicore not supported

Merge pull request !1079 from lianliguang/convert-to-AICPU-when-AiCore-not-supported-kernel

mindspore/ccsrc/common/trans.cc

@@ -85,7 +85,7 @@ const std::map<TypeId, size_t> type_map = {{kNumberTypeBool, 1},    {kNumberType
   } while (0)
 
 template <typename T>
-T Ceil(T n1, T n2) {
+T DivCeil(T n1, T n2) {
   return (n2 != 0) ? (n1 - 1) / n2 + 1 : 0;
 }
 
@@ -371,15 +371,48 @@ std::vector<size_t> C1hwncoc0DeviceShape(const std::vector<size_t> &shape) {
   device_shape.push_back(kCubeSize);
   return device_shape;
 }
+
+std::vector<size_t> FracZc04DeviceShape(const std::vector<size_t> &shape) {
+  if (!CheckDims(shape)) {
+    MS_LOG(EXCEPTION) << "Check dims failed.";
+  }
+  std::vector<size_t> device_shape;
+  size_t c0 = 4;
+  size_t first_dim = DivCeil(c0 * shape[2] * shape[3], kCubeSize);
+  size_t no = DivCeil(DivCeil(shape[0], kCubeSize) * kCubeSize, kCubeSize);
+  device_shape.push_back(first_dim);
+  device_shape.push_back(no);
+  device_shape.push_back(kCubeSize);
+  device_shape.push_back(kCubeSize);
+  return device_shape;
+}
+
+std::vector<size_t> Nc1hwc04DeviceShape(const std::vector<size_t> &shape) {
+  if (!CheckDims(shape)) {
+    MS_LOG(EXCEPTION) << "Check dims failed.";
+  }
+  std::vector<size_t> device_shape;
+  size_t C1 = 1;
+  size_t C0 = 4;
+  device_shape.push_back(shape[0]);
+  device_shape.push_back(C1);
+  device_shape.push_back(shape[2]);
+  device_shape.push_back(shape[3]);
+  device_shape.push_back(C0);
+  return device_shape;
+}
 }  // namespace
 
 std::vector<size_t> TransShapeToDevice(const std::vector<size_t> &shape, const std::string &format) {
   using DeviceShapeTransfer = std::function<std::vector<size_t>(const std::vector<size_t> &)>;
-  const std::map<std::string, DeviceShapeTransfer> device_shape_map{
-    {kOpFormat_NCHW, NchwDeviceShape},       {kOpFormat_NHWC, NhwcDeviceShape},
-    {kOpFormat_HWCN, HwchDeviceShape},       {kOpFormat_FRAC_Z, FracZDeviceShape},
-    {kOpFormat_NC1HWC0, Nc1hwc0DeviceShape}, {kOpFormat_C1HWNCoC0, C1hwncoc0DeviceShape},
-  };
+  const std::map<std::string, DeviceShapeTransfer> device_shape_map{{kOpFormat_NCHW, NchwDeviceShape},
+                                                                    {kOpFormat_NHWC, NhwcDeviceShape},
+                                                                    {kOpFormat_HWCN, HwchDeviceShape},
+                                                                    {kOpFormat_FRAC_Z, FracZDeviceShape},
+                                                                    {kOpFormat_NC1HWC0, Nc1hwc0DeviceShape},
+                                                                    {kOpFormat_C1HWNCoC0, C1hwncoc0DeviceShape},
+                                                                    {kOpFormat_FRACTAL_Z_C04, FracZc04DeviceShape},
+                                                                    {kOpFormat_NC1HWC0_C04, Nc1hwc04DeviceShape}};
 
   if (format == kOpFormat_ND || format == kOpFormat_DEFAULT) {
     return shape;
@@ -506,13 +539,13 @@ bool NchwToFracZ(const FormatArgs &args, void *result) {
     MS_LOG(ERROR) << "Illegal dtype.";
     return false;
   }
-  size_t c1 = Ceil(c, c0);
+  size_t c1 = DivCeil(c, c0);
   size_t hw = h * w;
   size_t chw = c * hw;
   size_t hwc0 = hw * c0;
   size_t nchw = n * chw;
 
-  size_t hf_cnt = Ceil(n, kCubeSize);
+  size_t hf_cnt = DivCeil(n, kCubeSize);
   size_t vf_cnt = c1 * hw;
   size_t fractal_ele_cnt = c0 * kCubeSize;
   size_t total_ele_cnt = hf_cnt * vf_cnt * fractal_ele_cnt;
@@ -775,7 +808,7 @@ bool NchwToNc1hwc0(const FormatArgs &args, void *result) {
     MS_LOG(ERROR) << "Illegal dtype.";
     return false;
   }
-  size_t c1 = Ceil(c, c0);
+  size_t c1 = DivCeil(c, c0);
   size_t hw = h * w;
   size_t chw = c * hw;
   size_t c1hwc0 = c1 * hw * c0;

mindspore/ccsrc/device/ascend/kernel_select_ascend.cc

@@ -34,6 +34,7 @@ namespace ascend {
 namespace {
 const float kWegihtBaseScore = 1;
 const float kFeatureMapBaseScore = 10;
+constexpr auto kPriChoosenFormat = "pri_format";
 enum MatchCountPriority : int {
   MATCH_COUNT_PRIORITY_BEGIN = 0,
   MATCH_DTYPE_COUNT = MATCH_COUNT_PRIORITY_BEGIN,
@@ -85,6 +86,7 @@ string GetPriorityMatchFormat(const CNodePtr &cnode) {
   if (need_change_nd) {
     priority_matched_format = kOpFormat_DEFAULT;
   }
+  AnfAlgo::SetNodeAttr(kPriChoosenFormat, MakeValue(priority_matched_format), cnode);
   return priority_matched_format;
 }
 /**
@@ -394,9 +396,9 @@ void PrintRaiseOrReducePrecisionSelectedInfo(const CNodePtr &cnode,
   std::ostringstream buffer;
   buffer << cnode->DebugString();
   if (precision_reduce) {
-    buffer << " reduce precision, node datatype: ";
+    buffer << " reduce precision, node datatype: \n";
   } else {
-    buffer << " raise precision, node datatype: ";
+    buffer << " raise precision, node datatype: \n";
   }
   PrintInputAndOutputInferType(buffer, cnode);
   buffer << ", select kernel:" << selected_kernel_build_info->ToString();
@@ -464,66 +466,57 @@ std::vector<std::shared_ptr<kernel::KernelBuildInfo>> FilterRaisedOrReducePrecis
 }
 }  // namespace
 
-std::shared_ptr<kernel::KernelBuildInfo> CanHitKernelInfo(
-  int *status, const CNodePtr &kernel_node,
-  const std::vector<std::shared_ptr<kernel::KernelBuildInfo>> &kernel_info_list) {
+KernelSelectStatus SetMatchedKernelInfo(const CNodePtr &kernel_node,
+                                        const std::vector<std::shared_ptr<kernel::KernelBuildInfo>> &kernel_info_list) {
   MS_EXCEPTION_IF_NULL(kernel_node);
+  KernelSelectStatus select_status = kNoMatched;
   bool precision_reduce = false;
   std::shared_ptr<kernel::KernelBuildInfo> selected_kernel_info = nullptr;
+  // Matched kernel info
+  // Filter kernel info matched with me infered type
   auto filtered_kernel_info_list = GetAllMatchedFilteredKernelInfo(kernel_node, kernel_info_list);
   if (!filtered_kernel_info_list.empty()) {
     selected_kernel_info = ChooseMatchedKernelInfo(kernel_node, filtered_kernel_info_list);
+    select_status = kStatusAllMatched;
   } else {
     // selected kernel info using raised precision or reduce precision
     filtered_kernel_info_list =
       FilterRaisedOrReducePrecisionMatchedKernelInfo(kernel_node, kernel_info_list, &precision_reduce);
     selected_kernel_info = ChooseMatchedKernelInfo(kernel_node, filtered_kernel_info_list);
     if (selected_kernel_info == nullptr) {
-      return nullptr;
+      return select_status;
     } else {
       PrintRaiseOrReducePrecisionSelectedInfo(kernel_node, selected_kernel_info, precision_reduce);
-      *status = precision_reduce ? kStatusReducePrecision : kStatusRaisePrecision;
+      select_status = precision_reduce ? kStatusReducePrecision : kStatusRaisePrecision;
     }
   }
-  return selected_kernel_info;
+  // Set kernel info to the anfnode
+  AnfAlgo::SetSelectKernelBuildInfo(selected_kernel_info, kernel_node.get());
+  // Set format and data type for input tensor.
+  SetTensorDeviceInfo(*selected_kernel_info, kernel_node);
+  return select_status;
 }
 
-int SelectKernelInfo(const CNodePtr &kernel_node) {
+KernelSelectStatus SelectKernelInfo(const CNodePtr &kernel_node) {
   std::vector<std::shared_ptr<kernel::KernelBuildInfo>> kernel_info_list;
-  int status = kStatusAllMatched;
   MS_EXCEPTION_IF_NULL(kernel_node);
   kernel::KernelQuery(kernel_node, &kernel_info_list);
-  // filter kernel info matched with me infered type
-  auto selected_kernel_info = CanHitKernelInfo(&status, kernel_node, kernel_info_list);
-  if (selected_kernel_info == nullptr) {
+  auto select_status = SetMatchedKernelInfo(kernel_node, kernel_info_list);
+  // If aicore not find valid kernel info reloading aicpu kernel info list to find it
+  if (select_status == kNoMatched) {
     MS_LOG(WARNING) << "The node [" << kernel_node->DebugString()
                     << "] cannot find valid TBE kernel info, try to get aicpu kernel info";
-    kernel::AicpuQuery(kernel_node, &kernel_info_list);
-    selected_kernel_info = CanHitKernelInfo(&status, kernel_node, kernel_info_list);
+    kernel::AICpuQuery(kernel_node, &kernel_info_list);
+    select_status = SetMatchedKernelInfo(kernel_node, kernel_info_list);
   }
-  if (selected_kernel_info == nullptr) {
+  // The kernel info not finded both in the aicpu kernel list & aicore kernel list
+  if (select_status == kNoMatched) {
     std::ostringstream buffer;
     PrintInputAndOutputInferType(buffer, kernel_node);
     MS_EXCEPTION(TypeError) << "The node [" << kernel_node->DebugString()
                             << "] cannot find valid kernel info, not supported the type " << buffer.str();
   }
-  AnfAlgo::SetSelectKernelBuildInfo(selected_kernel_info, kernel_node.get());
-  // Set format and data type for input tensor.
-  SetTensorDeviceInfo(*selected_kernel_info, kernel_node);
-  return status;
-}
-
-bool CheckKernelAccuracySupported(const CNodePtr &kernel_node,
-                                  const kernel::KernelBuildInfoPtr &new_kernel_build_info) {
-  MS_EXCEPTION_IF_NULL(kernel_node);
-  std::vector<std::shared_ptr<kernel::KernelBuildInfo>> kernel_info_list;
-  kernel::KernelQuery(kernel_node, &kernel_info_list);
-  auto result = std::find_if(kernel_info_list.begin(), kernel_info_list.end(),
-                             [&new_kernel_build_info](const kernel::KernelBuildInfoPtr item) {
-                               MS_EXCEPTION_IF_NULL(item);
-                               return *item == *new_kernel_build_info;
-                             });
-  return result != kernel_info_list.end();
+  return select_status;
 }
 }  // namespace ascend
 }  // namespace device

mindspore/ccsrc/device/ascend/kernel_select_ascend.h

@@ -21,8 +21,13 @@
 namespace mindspore {
 namespace device {
 namespace ascend {
-int SelectKernelInfo(const CNodePtr &kernel_node);
-bool CheckKernelAccuracySupported(const CNodePtr &kernel_node, const kernel::KernelBuildInfoPtr &new_kernel_build_info);
+enum KernelSelectStatus {
+  kNoMatched = -1,
+  kStatusAllMatched = 0,
+  kStatusReducePrecision = 1,
+  kStatusRaisePrecision = 2,
+};
+KernelSelectStatus SelectKernelInfo(const CNodePtr &kernel_node);
 }  // namespace ascend
 }  // namespace device
 }  // namespace mindspore

mindspore/ccsrc/kernel/hccl/hccl_kernel_metadata.cc

@@ -35,7 +35,7 @@ void HcclMetadataInfo(const CNodePtr &kernel_node, std::vector<std::shared_ptr<K
   std::vector<std::string> input_format, output_format;
   std::vector<TypeId> input_type, output_type;
   for (const auto &data_type : data_type_list) {
-    for (const auto &format : k4DSupportFormat) {
+    for (const auto &format : kOpFormatList) {
       auto builder = std::make_shared<KernelBuildInfo::KernelBuildInfoBuilder>();
       input_format.clear();
       input_format.push_back(format);

mindspore/ccsrc/kernel/kernel_query.cc

@@ -35,14 +35,18 @@ void FilterInvalidKernelInfo(const CNodePtr &kernel_node,
                        return AnfAlgo::GetOutputTensorNum(kernel_node) == kernel_build_info->GetOutputNum() &&
                               AnfAlgo::GetInputTensorNum(kernel_node) == kernel_build_info->GetInputNum();
                      });
-  kernel_info_list->clear();
   if (!filtered_list.empty()) {
+    kernel_info_list->clear();
     (void)std::copy(filtered_list.begin(), filtered_list.end(), std::back_inserter(*kernel_info_list));
   } else {
-    MS_LOG(EXCEPTION) << "node" << kernel_node->DebugString() << "'s output size : ["
-                      << AnfAlgo::GetOutputTensorNum(kernel_node) << "]"
-                      << "input size : [" << AnfAlgo::GetInputTensorNum(kernel_node)
-                      << "] cannot match any kernelInfo !";
+    MS_LOG(WARNING) << "All kernel Info list does not match any kernel info ";
+    for (size_t index; index < kernel_info_list->size(); ++index) {
+      MS_EXCEPTION_IF_NULL(kernel_info_list->at(index));
+      MS_LOG(WARNING) << "kernel [ " << index << " ] :" << kernel_info_list->at(index)->ToString();
+    }
+    MS_LOG(WARNING) << "node" << kernel_node->DebugString() << "'s output size : ["
+                    << AnfAlgo::GetOutputTensorNum(kernel_node) << "]"
+                    << "input size : [" << AnfAlgo::GetInputTensorNum(kernel_node) << "] cannot match any kernelInfo !";
   }
 }
 }  // namespace
@@ -50,7 +54,6 @@ void KernelQuery(const CNodePtr &kernel_node, std::vector<std::shared_ptr<kernel
   MS_EXCEPTION_IF_NULL(kernel_node);
   MS_EXCEPTION_IF_NULL(kernel_info_list);
   TbeMetadataInfo(kernel_node, kernel_info_list);
-
   if (kernel_info_list->empty()) {
     AicpuMetadataInfo(kernel_node, kernel_info_list);
   }
@@ -68,12 +71,41 @@ void KernelQuery(const CNodePtr &kernel_node, std::vector<std::shared_ptr<kernel
   FilterInvalidKernelInfo(kernel_node, kernel_info_list);
 }
 
-void AicpuQuery(const CNodePtr &kernel_node, std::vector<std::shared_ptr<kernel::KernelBuildInfo>> *kernel_info_list) {
+void AICpuQuery(const CNodePtr &kernel_node, std::vector<std::shared_ptr<kernel::KernelBuildInfo>> *kernel_info_list) {
   MS_EXCEPTION_IF_NULL(kernel_node);
   MS_EXCEPTION_IF_NULL(kernel_info_list);
   kernel_info_list->clear();
   AicpuMetadataInfo(kernel_node, kernel_info_list);
   FilterInvalidKernelInfo(kernel_node, kernel_info_list);
 }
+bool IsSupportedByAiCpu(const AnfNodePtr &kernel_node, const KernelBuildInfoPtr &select_kernel_build_info) {
+  MS_EXCEPTION_IF_NULL(kernel_node);
+  MS_EXCEPTION_IF_NULL(select_kernel_build_info);
+  std::vector<std::shared_ptr<kernel::KernelBuildInfo>> kernel_info_list;
+  auto cnode = kernel_node->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(cnode);
+  AicpuMetadataInfo(cnode, &kernel_info_list);
+  FilterInvalidKernelInfo(cnode, &kernel_info_list);
+  return std::any_of(kernel_info_list.begin(), kernel_info_list.end(),
+                     [&select_kernel_build_info](const kernel::KernelBuildInfoPtr item) {
+                       MS_EXCEPTION_IF_NULL(item);
+                       return *item == *select_kernel_build_info;
+                     });
+}
+
+bool IsSupportedByAiCore(const AnfNodePtr &kernel_node, const KernelBuildInfoPtr &select_kernel_build_info) {
+  MS_EXCEPTION_IF_NULL(kernel_node);
+  MS_EXCEPTION_IF_NULL(select_kernel_build_info);
+  std::vector<std::shared_ptr<kernel::KernelBuildInfo>> kernel_info_list;
+  auto cnode = kernel_node->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(cnode);
+  TbeMetadataInfo(cnode, &kernel_info_list);
+  FilterInvalidKernelInfo(cnode, &kernel_info_list);
+  return std::any_of(kernel_info_list.begin(), kernel_info_list.end(),
+                     [&select_kernel_build_info](const kernel::KernelBuildInfoPtr item) {
+                       MS_EXCEPTION_IF_NULL(item);
+                       return *item == *select_kernel_build_info;
+                     });
+}
 }  // namespace kernel
 }  // namespace mindspore

mindspore/ccsrc/kernel/kernel_query.h

@@ -26,7 +26,9 @@
 namespace mindspore {
 namespace kernel {
 void KernelQuery(const CNodePtr &kernel_node, std::vector<std::shared_ptr<kernel::KernelBuildInfo>> *kernel_info_list);
-void AicpuQuery(const CNodePtr &kernel_node, std::vector<std::shared_ptr<kernel::KernelBuildInfo>> *kernel_info_list);
+void AICpuQuery(const CNodePtr &kernel_node, std::vector<std::shared_ptr<kernel::KernelBuildInfo>> *kernel_info_list);
+bool IsSupportedByAiCpu(const AnfNodePtr &kernel_node, const KernelBuildInfoPtr &select_kernel_build_info);
+bool IsSupportedByAiCore(const AnfNodePtr &kernel_node, const KernelBuildInfoPtr &select_kernel_build_info);
 }  // namespace kernel
 }  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_KERNEL_KERNEL_QUERY_H_

mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc

@@ -551,11 +551,6 @@ bool ParseMetadata(const CNodePtr &kernel_node, const std::shared_ptr<const OpIn
 }
 
 bool IsShapeMatchFormat(const std::vector<size_t> &shape, const std::string &format) {
-  const std::set<std::string> kOpFormatList = {kOpFormat_DEFAULT, kOpFormat_NC1KHKWHWC0, kOpFormat_ND,
-                                               kOpFormat_NCHW,    kOpFormat_NHWC,        kOpFormat_HWCN,
-                                               kOpFormat_NC1HWC0, kOpFormat_FRAC_Z,      kOpFormat_C1HWNCoC0,
-                                               kOpFormat_FRAC_NZ, kOpFormat_NC1HWC0_C04, kOpFormat_FRACTAL_Z_C04};
-
   // if format is default, it remarkes support all format
   if (kOpFormatList.find(format) == kOpFormatList.end()) {
     MS_LOG(EXCEPTION) << "Got the unknown format " << format;

mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc

@@ -54,6 +54,7 @@
 #include "pre_activate/pass/optimize_dependence.h"
 #include "pre_activate/pass/erase_visit_attr.h"
 #include "pre_activate/ascend/format_type/insert_cast.h"
+#include "pre_activate/ascend/format_type/convert_unsupported_transnode_to_aicpu.h"
 #include "pre_activate/pass/eliminate_redundant_op.h"
 #include "pre_activate/pass/common_subexpression_elimination.h"
 #include "pre_activate/ascend/format_type/merge_cast_to_op.h"
@@ -172,6 +173,7 @@ void AscendMixPrecision(const std::shared_ptr<session::KernelGraph> &kernel_grap
   mixed_precision_pm->AddPass(std::make_shared<MergeCastToOp>());
   mixed_precision_pm->AddPass(std::make_shared<LayerNormBetaGammaBackpropFusion>());
   mixed_precision_pm->AddPass(std::make_shared<EraseVisitAttr>());
+  mixed_precision_pm->AddPass(std::make_shared<ConvertUnSupportNodeToAICPU>());
   optimizer->AddPassManager(mixed_precision_pm);
   (void)optimizer->Optimize(kernel_graph);
   kernel_graph->SetExecOrderByDefault();

mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc

@@ -268,6 +268,7 @@ AnfNodePtr AddCastOpNodeToGraph(const FuncGraphPtr &func_graph, const AnfNodePtr
   }
   AnfAlgo::SetSelectKernelBuildInfo(builder.Build(), cast.get());
   AnfAlgo::SetOutputInferTypeAndShape({origin_type}, {origin_shape}, cast.get());
+  AnfAlgo::SetNodeAttr(kIsBackendCast, MakeValue(true), cast);
   return cast;
 }
 

mindspore/ccsrc/pre_activate/ascend/ascend_helper.h

@@ -30,10 +30,6 @@ class KernelSelect {
   KernelSelect() = default;
   virtual ~KernelSelect() = default;
   virtual void SelectKernel(const CNodePtr &cnode) { device::ascend::SelectKernelInfo(cnode); }
-  virtual bool CheckKernelAccuracySupported(const CNodePtr &kernel_node,
-                                            const kernel::KernelBuildInfoPtr &new_kernel_build_info) {
-    return device::ascend::CheckKernelAccuracySupported(kernel_node, new_kernel_build_info);
-  }
 };
 using KernelSelectPtr = std::shared_ptr<KernelSelect>;
 
@@ -41,8 +37,13 @@ class SupportedChecker {
  public:
   SupportedChecker() = default;
   virtual ~SupportedChecker() = default;
-  virtual bool CheckSupported(const AnfNodePtr &anf_node, const kernel::KernelBuildInfoPtr &select_kernel_build_info) {
-    return kernel::CheckSupported(anf_node, select_kernel_build_info);
+  virtual bool CheckAiCoreSupported(const AnfNodePtr &anf_node,
+                                    const kernel::KernelBuildInfoPtr &select_kernel_build_info) {
+    return kernel::IsSupportedByAiCore(anf_node, select_kernel_build_info);
+  }
+  virtual bool CheckAiCpuSupported(const AnfNodePtr &anf_node,
+                                   const kernel::KernelBuildInfoPtr &select_kernel_build_info) {
+    return kernel::IsSupportedByAiCpu(anf_node, select_kernel_build_info);
   }
 };
 using SupportedCheckerPtr = std::shared_ptr<SupportedChecker>;

mindspore/ccsrc/pre_activate/ascend/format_type/convert_unsupported_transnode_to_aicpu.cc

@@ -0,0 +1,54 @@
+/**
+ * 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 "pre_activate/ascend/format_type/convert_unsupported_transnode_to_aicpu.h"
+#include <memory>
+#include "session/anf_runtime_algorithm.h"
+#include "kernel/kernel_build_info.h"
+#include "kernel/kernel_query.h"
+namespace mindspore {
+namespace opt {
+const BaseRef ConvertUnSupportNodeToAICPU::DefinePattern() const {
+  VarPtr X = std::make_shared<Var>();
+  VarPtr Xs = std::make_shared<SeqVar>();
+  return VectorRef({X, Xs});
+}
+
+const AnfNodePtr ConvertUnSupportNodeToAICPU::Process(const mindspore::FuncGraphPtr &,
+                                                      const mindspore::AnfNodePtr &node,
+                                                      const mindspore::EquivPtr &) const {
+  if (node == nullptr || !node->isa<CNode>()) {
+    return nullptr;
+  }
+  auto node_name = AnfAlgo::GetCNodeName(node);
+  if (node_name != prim::KPrimTransData->name() || node_name != prim::kPrimCast->name()) {
+    return nullptr;
+  }
+  auto kernel_builder_info = AnfAlgo::GetSelectKernelBuildInfo(node);
+  if (supported_checker_->CheckAiCoreSupported(node, kernel_builder_info)) {
+    return node;
+  } else if (supported_checker_->CheckAiCpuSupported(node, kernel_builder_info)) {
+    auto builder = std::make_shared<kernel::KernelBuildInfo::KernelBuildInfoBuilder>(kernel_builder_info);
+    builder->SetKernelType(AICPU_KERNEL);
+    AnfAlgo::SetSelectKernelBuildInfo(builder->Build(), node.get());
+  } else {
+    MS_LOG(EXCEPTION) << " kernel " << kernel_builder_info->ToString() << "is not supported in AiCPU & AiCore : node ["
+                      << node->DebugString() << "]";
+  }
+  return node;
+}
+}  // namespace opt
+}  // namespace mindspore

mindspore/ccsrc/pre_activate/ascend/format_type/convert_unsupported_transnode_to_aicpu.h

@@ -0,0 +1,37 @@
+/**
+ * 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 <memory>
+#include "pre_activate/common/optimizer.h"
+#include "pre_activate/ascend/ascend_helper.h"
+#ifndef MINDSPORE_CONVERT_UNSUPPORTED_NODE_TO_AICPU_H
+#define MINDSPORE_CONVERT_UNSUPPORTED_NODE_TO_AICPU_H
+namespace mindspore {
+namespace opt {
+class ConvertUnSupportNodeToAICPU : public PatternProcessPass {
+ public:
+  explicit ConvertUnSupportNodeToAICPU(bool multigraph = true)
+      : PatternProcessPass("convert_unsupported_node_to_aicpu", multigraph),
+        supported_checker_(std::make_shared<SupportedChecker>()) {}
+  ~ConvertUnSupportNodeToAICPU() override = default;
+  const BaseRef DefinePattern() const override;
+  const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
+
+ private:
+  SupportedCheckerPtr supported_checker_;
+};
+}  // namespace opt
+}  // namespace mindspore
+#endif  // MINDSPORE_CONVERT_UNSUPPORTED_NODE_TO_AICPU_H

mindspore/ccsrc/pre_activate/ascend/format_type/insert_cast_for_runop.h

@@ -13,8 +13,8 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#ifndef MINDSPORE_CCSRC_DEVICE_OPTIMIZER_FORMAT_TYPE_PASS_INSERT_CAST_FOR_RUNOP_H_
-#define MINDSPORE_CCSRC_DEVICE_OPTIMIZER_FORMAT_TYPE_PASS_INSERT_CAST_FOR_RUNOP_H_
+#ifndef MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_FORMAT_TYPE_INSERT_CAST_FOR_RUNOP_H_
+#define MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_FORMAT_TYPE_INSERT_CAST_FOR_RUNOP_H_
 #include <string>
 
 #include "pre_activate/common/optimizer.h"
@@ -32,4 +32,4 @@ class RunOpInsertCast : public PatternProcessPass {
 }  // namespace opt
 }  // namespace mindspore
 
-#endif  // MINDSPORE_CCSRC_DEVICE_OPTIMIZER_FORMAT_TYPE_PASS_INSERT_CAST_FOR_RUNOP_H_
+#endif  // MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_FORMAT_TYPE_INSERT_CAST_FOR_RUNOP_H_

mindspore/ccsrc/pre_activate/ascend/format_type/insert_transdata_for_runop.h

@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-#ifndef MINDSPORE_CCSRC_DEVICE_OPTIMIZER_FORMAT_TYPE_PASS_INSERT_TRANSDATA_FOR_RUNOP_H_
-#define MINDSPORE_CCSRC_DEVICE_OPTIMIZER_FORMAT_TYPE_PASS_INSERT_TRANSDATA_FOR_RUNOP_H_
+#ifndef MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_FORMAT_TYPE_INSERT_TRANSDATA_FOR_RUNOP_H_
+#define MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_FORMAT_TYPE_INSERT_TRANSDATA_FOR_RUNOP_H_
 
 #include <string>
 #include <utility>
@@ -41,4 +41,4 @@ class RunOpInsertTransData : public PatternProcessPass {
 }  // namespace opt
 }  // namespace mindspore
 
-#endif  // MINDSPORE_CCSRC_DEVICE_OPTIMIZER_FORMAT_TYPE_PASS_INSERT_TRANSDATA_FOR_RUNOP_H_
+#endif  // MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_FORMAT_TYPE_INSERT_TRANSDATA_FOR_RUNOP_H_

mindspore/ccsrc/pre_activate/ascend/ir_fission/topk_split.cc

@@ -128,7 +128,7 @@ const AnfNodePtr TopKSplit::Process(const FuncGraphPtr &func_graph, const AnfNod
   auto indices_const = CreateValueNode(new_cnode);
   new_cnode->add_input(indices_const);
   MS_EXCEPTION_IF_NULL(supported_checker_);
-  if (!supported_checker_->CheckSupported(new_cnode, CreateKernelBuildInfo())) {
+  if (!supported_checker_->CheckAiCoreSupported(new_cnode, CreateKernelBuildInfo())) {
     return nullptr;
   }
 

mindspore/ccsrc/pre_activate/ascend/ir_fusion/transpose_transdata_fusion.cc

@@ -53,7 +53,7 @@ const AnfNodePtr TransposeTransDataFusion::Process(const FuncGraphPtr &func_grap
   new_transdata_builder->SetProcessor(transdata_kernel_build_info->processor());
 
   auto new_fusion_transdata = std::make_shared<Primitive>(kTransDataOpName);
-  if (kernel_select_->CheckKernelAccuracySupported(transdata_cnode, new_transdata_builder->Build())) {
+  if (supported_checker_->CheckAiCoreSupported(transdata_cnode, new_transdata_builder->Build())) {
     std::vector<AnfNodePtr> inputs = {NewValueNode(new_fusion_transdata),
                                       utils::cast<AnfNodePtr>((*equiv)[input_varptr_])};
     auto new_node = func_graph->NewCNode(inputs);

mindspore/ccsrc/pre_activate/ascend/ir_fusion/transpose_transdata_fusion.h

@@ -34,7 +34,7 @@ class TransposeTransDataFusion : public PatternProcessPass {
   explicit TransposeTransDataFusion(bool multigraph = true)
       : PatternProcessPass("transpose_transdata_fusion", multigraph) {
     input_varptr_ = std::make_shared<Var>();
-    kernel_select_ = std::make_shared<KernelSelect>();
+    supported_checker_ = std::make_shared<SupportedChecker>();
   }
   ~TransposeTransDataFusion() override = default;
   const BaseRef DefinePattern() const override;
@@ -42,7 +42,9 @@ class TransposeTransDataFusion : public PatternProcessPass {
 
  private:
   VarPtr input_varptr_;
-  KernelSelectPtr kernel_select_;
+
+ private:
+  SupportedCheckerPtr supported_checker_;
 };
 }  // namespace opt
 }  // namespace mindspore

mindspore/ccsrc/session/ascend_session.cc

@@ -329,9 +329,9 @@ void AscendSession::SelectKernel(const KernelGraph &kernel_graph) const {
   size_t reduce_precision_count = 0;
   for (const auto &cnode : kernel_graph.execution_order()) {
     auto status = device::ascend::SelectKernelInfo(cnode);
-    if (status == kStatusRaisePrecision) {
+    if (status == device::ascend::kStatusRaisePrecision) {
       raise_precision_count++;
-    } else if (status == kStatusReducePrecision) {
+    } else if (status == device::ascend::kStatusReducePrecision) {
       reduce_precision_count++;
     }
     MS_LOG(INFO) << "Select ApplyKernel: " << cnode->DebugString();

mindspore/ccsrc/session/kernel_graph.cc

@@ -27,6 +27,8 @@
 namespace mindspore {
 namespace session {
 namespace {
+constexpr auto kIsFeatureMapOutput = "IsFeatureMapOutput";
+constexpr auto kIsFeatureMapInputList = "IsFeatureMapInputList";
 void PushNoVisitedNode(const AnfNodePtr &node, std::queue<AnfNodePtr> *que,
                        std::unordered_set<AnfNodePtr> *visited_nodes) {
   MS_EXCEPTION_IF_NULL(que);
@@ -180,11 +182,24 @@ CNodePtr KernelGraph::NewCNode(const std::vector<AnfNodePtr> &inputs) {
   cnode->set_abstract(std::make_shared<abstract::AbstractNone>());
   // create kernel_info from new parameter
   auto kernel_info = std::make_shared<device::KernelInfo>();
+  std::vector<size_t> feature_map_input_indexs;
   // if the node only has the primitive(such as getNext) or the node's input has a feature map input
   // then the node's output is a feature map output
-  if (inputs.size() == 1 || std::any_of(inputs.begin() + 1, inputs.end(),
-                                        [&](const AnfNodePtr &node) { return AnfAlgo::IsFeatureMapOutput(node); })) {
+  for (size_t index = 1; index < inputs.size(); ++index) {
+    auto node = inputs[index];
+    if (AnfAlgo::IsFeatureMapOutput(node)) {
+      feature_map_input_indexs.push_back(index);
+    }
+  }
+  if (AnfAlgo::GetCNodeName(cnode) == prim::kPrimCast->name()) {
+    AnfAlgo::SetNodeAttr(kIsBackendCast, MakeValue(false), cnode);
+  }
+  if (inputs.size() == 1 || !feature_map_input_indexs.empty()) {
     kernel_info->SetFeatureMapFlag(true);
+    AnfAlgo::SetNodeAttr(kIsFeatureMapOutput, MakeValue(true), cnode);
+    AnfAlgo::SetNodeAttr(kIsFeatureMapInputList, MakeValue(feature_map_input_indexs), cnode);
+  } else {
+    AnfAlgo::SetNodeAttr(kIsFeatureMapOutput, MakeValue(false), cnode);
   }
   cnode->set_kernel_info(kernel_info);
   AnfAlgo::SetGraphId(graph_id_, cnode.get());

mindspore/ccsrc/utils/utils.h

@@ -142,6 +142,7 @@ constexpr auto kLabelGotoOpName = "LabelGoto";
 
 // attr key name
 constexpr auto kAttrInputNames = "input_names";
+constexpr auto kIsBackendCast = "is_backed_cast";
 constexpr auto kAttrOutputNames = "output_names";
 constexpr auto kAttrVisited = "visited";
 constexpr auto kAttrShape = "shape";
@@ -201,10 +202,6 @@ constexpr auto kControlDependBehindIndex = 2;
 // index define of depend
 constexpr auto kRealInputIndexInDepend = 1;
 constexpr auto kDependAttachNodeIndex = 2;
-// status of kernel select result
-const int kStatusReducePrecision = -1;
-const int kStatusRaisePrecision = 1;
-const int kStatusAllMatched = 0;
 // format
 constexpr auto kOpFormat_DEFAULT = "DefaultFormat";
 constexpr auto kOpFormat_NC1KHKWHWC0 = "NC1KHKWHWC0";
@@ -218,18 +215,11 @@ constexpr auto kOpFormat_FRAC_NZ = "FRACTAL_NZ";
 constexpr auto kOpFormat_C1HWNCoC0 = "C1HWNCoC0";
 constexpr auto kOpFormat_NC1HWC0_C04 = "NC1HWC0_C04";
 constexpr auto kOpFormat_FRACTAL_Z_C04 = "FRACTAL_Z_C04";
-const std::set<std::string> k1DSupportFormat = {kOpFormat_DEFAULT,  kOpFormat_NCHW,        kOpFormat_NHWC,
-                                                kOpFormat_FRAC_Z,   kOpFormat_NC1KHKWHWC0, kOpFormat_NC1HWC0,
-                                                kOpFormat_C1HWNCoC0};
-
-const std::set<std::string> k2DSupportFormat = {kOpFormat_DEFAULT, kOpFormat_NCHW, kOpFormat_NHWC, kOpFormat_FRAC_Z,
-                                                kOpFormat_NC1KHKWHWC0};
-const std::set<std::string> k3DSupportFormat = {kOpFormat_DEFAULT, kOpFormat_NC1KHKWHWC0};
-const std::set<std::string> k4DSupportFormat = k1DSupportFormat;
-const std::vector<std::set<std::string>> kShapeSupportFormatMap = {k1DSupportFormat, k2DSupportFormat, k3DSupportFormat,
-                                                                   k4DSupportFormat};
+const std::set<std::string> kOpFormatList = {kOpFormat_DEFAULT, kOpFormat_NC1KHKWHWC0, kOpFormat_ND,
+                                             kOpFormat_NCHW,    kOpFormat_NHWC,        kOpFormat_HWCN,
+                                             kOpFormat_NC1HWC0, kOpFormat_FRAC_Z,      kOpFormat_C1HWNCoC0,
+                                             kOpFormat_FRAC_NZ, kOpFormat_NC1HWC0_C04, kOpFormat_FRACTAL_Z_C04};
 const std::set<std::string> kDefaultCompatibleFormat = {kOpFormat_ND, kOpFormat_NCHW, kOpFormat_NHWC, kOpFormat_HWCN};
-
 const std::set<std::string> kOptOperatorSet = {
   kMomentumOpName,       kApplyMomentumOpName,        kApplyAdadeltaOpName,
   kApplyAdagradOpName,   kApplyAdagradDAName,         kApplyAdamOpName,

tests/ut/cpp/device/ascend_kernel_select_test.cc

@@ -1,345 +0,0 @@
-/**
- * Copyright 2019 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 "mindspore/ccsrc/device/ascend/kernel_select_ascend.h"
-#include "common/common_test.h"
-#include "session/kernel_graph.h"
-#include "kernel/kernel.h"
-#include "session/anf_runtime_algorithm.h"
-#include "utils/utils.h"
-#include "operator/ops.h"
-#include "mindspore/ccsrc/device/kernel_info.h"
-#include "mindspore/ccsrc/kernel/kernel_build_info.h"
-#include <vector>
-namespace mindspore {
-namespace device {
-namespace ascend {
-namespace {
-using KernelInfo = device::KernelInfo;
-using KernelBuildInfoBuilder = kernel::KernelBuildInfo::KernelBuildInfoBuilder;
-using KernelBuildInfo = kernel::KernelBuildInfo;
-using KernelGraph = session::KernelGraph;
-using KernelBuildInfoPtr = std::shared_ptr<KernelBuildInfo>;
-using KernelBuilderPtr = std::shared_ptr<KernelBuildInfoBuilder>;
-using Shape = std::vector<size_t>;
-using ShapeList = std::vector<Shape>;
-enum MatchCountPriority {
-  MATCH_COUNT_PRIORITY_BEGIN = 0,
-  MATCH_FORMAT_COUNT = MATCH_COUNT_PRIORITY_BEGIN,
-  MATCH_DTYPE_COUNT,
-  MATCH_NZ_FORMAT_COUNT,
-  MATCH_5D_FORMAT_COUNT,
-  MATCH_OUTPUT_DTYPE_COUNT,
-  MATCH_COUNT_PRIORITY_END
-};
-
-const std::set<std::string> kOpFormatList = {
-  kOpFormat_DEFAULT, kOpFormat_NC1KHKWHWC0, kOpFormat_ND,     kOpFormat_NCHW,      kOpFormat_NHWC,
-  kOpFormat_HWCN,    kOpFormat_NC1HWC0,     kOpFormat_FRAC_Z, kOpFormat_C1HWNCoC0, kOpFormat_FRAC_NZ};
-
-bool IsShapeMatchFormat(const std::vector<size_t> &shape, const std::string &format) {
-  // if format is default,it remarkes support all format
-  if (kOpFormatList.find(format) == kOpFormatList.end()) {
-    MS_EXCEPTION(ArgumentError) << "got the unknow format " << format;
-  }
-  if (format == kOpFormat_DEFAULT) {
-    return true;
-  }
-  // if shape size is 0,the shape will be a scalar
-  if (shape.empty()) {
-    return true;
-  }
-  if (shape.size() > kShapeSupportFormatMap.size()) {
-    return false;
-  }
-  if (format == kOpFormat_FRAC_NZ && shape.size() >= 2) {
-    return shape[shape.size() - 1] % 16 != 0 && shape[shape.size() - 2] % 16 != 0;
-  }
-  return !(kShapeSupportFormatMap[shape.size() - 1].find(format) == kShapeSupportFormatMap[shape.size() - 1].end());
-}
-
-bool IsValidKernelInfo(const std::shared_ptr<CNode> &kernel_node, const kernel::KernelBuildInfo &kernel_build_info) {
-  MS_EXCEPTION_IF_NULL(kernel_node);
-  auto check_function = [](const std::vector<size_t> &shape, const std::string &format) -> bool {
-    if (!IsShapeMatchFormat(shape, format)) {
-      return false;
-    }
-    for (auto shape_value : shape) {
-      if (shape_value == 0) {
-        MS_EXCEPTION(ArgumentError) << "dimension size of the tensor shape should be a positive integer, but got ["
-                                    << shape_value << "]";
-      }
-    }
-    return true;
-  };
-  for (size_t index = 0; index < kernel_build_info.GetOutputNum(); ++index) {
-    auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, index);
-    if (!check_function(output_shape, kernel_build_info.GetOutputFormat(index))) {
-      return false;
-    }
-  }
-  for (size_t index = 0; index < kernel_build_info.GetInputNum(); ++index) {
-    auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, index);
-    if (!check_function(input_shape, kernel_build_info.GetInputFormat(index))) {
-      return false;
-    }
-  }
-  return true;
-}
-
-bool MatchInferOutputDataType(const CNodePtr &cnode, const kernel::KernelBuildInfo &kernel_build_info) {
-  MS_EXCEPTION_IF_NULL(cnode);
-  // Check input data type
-  for (size_t input_index = 0; input_index < kernel_build_info.GetInputNum(); ++input_index) {
-    AnfNodePtr cur_input = cnode->input(input_index + 1);
-    MS_EXCEPTION_IF_NULL(cur_input);
-    TypeId input_origin_type;
-    if (cur_input->isa<Parameter>() && AnfAlgo::IsParameterWeight(cur_input->cast<ParameterPtr>())) {
-      // weight
-      input_origin_type = AnfAlgo::GetOutputDeviceDataType(cur_input, 0);
-    } else {
-      // feature map
-      input_origin_type = AnfAlgo::GetPrevNodeOutputInferDataType(cnode, input_index);
-    }
-    if (input_origin_type == kTypeUnknown) {
-      continue;
-    }
-    if (kernel_build_info.GetInputDeviceType(input_index) != input_origin_type) {
-      return false;
-    }
-  }
-  // Check output data type
-  for (size_t output_index = 0; output_index < kernel_build_info.GetOutputNum(); ++output_index) {
-    if (kernel_build_info.GetOutputDeviceType(output_index) != AnfAlgo::GetOutputInferDataType(cnode, output_index)) {
-      return false;
-    }
-  }
-  return true;
-}
-
-/**
- * compare too vector by priority,select a better vector,like compare too num,first compare highest num location,if
- * equal then next num location
- * example:[3,1,1,1] > [2,2,2,2] > [2,2,1,2] > [2,1,1,3]
- */
-bool PriorityChooseItem(const std::vector<int> &cur_item, std::vector<int> *best_item) {
-  MS_EXCEPTION_IF_NULL(best_item);
-  if (cur_item.size() != best_item->size()) {
-    MS_LOG(ERROR) << "item size should be same!";
-    return false;
-  }
-  // Update the best_item by comparing the cur_item and best_item
-  for (size_t i = 0; i < cur_item.size(); i++) {
-    if (cur_item[i] > best_item->at(i)) {
-      *best_item = cur_item;
-      return true;
-    } else if (cur_item[i] == best_item->at(i)) {
-      continue;
-    } else {
-      return false;
-    }
-  }
-  return false;
-}
-
-void UpdateCurMatchCounts(const kernel::KernelBuildInfo &kernel_build_info, const std::shared_ptr<CNode> &kernel_node,
-                          std::vector<int> *const cur_kernelinfo_match_counts) {
-  MS_EXCEPTION_IF_NULL(kernel_node);
-  MS_EXCEPTION_IF_NULL(cur_kernelinfo_match_counts);
-  if (cur_kernelinfo_match_counts->size() < MATCH_COUNT_PRIORITY_END) {
-    MS_EXCEPTION(ArgumentError) << "Out of range cur_kernelinfo_match_counts " << MATCH_COUNT_PRIORITY_END;
-  }
-  for (size_t input_index = 0; input_index < AnfAlgo::GetInputTensorNum(kernel_node); ++input_index) {
-    AnfNodePtr input_anf_node = kernel_node->input(input_index + 1);
-    MS_EXCEPTION_IF_NULL(input_anf_node);
-    // if a input parameter is a weight with default format, the input shouldn't participate the judge
-    if (input_anf_node->isa<Parameter>()) {
-      auto para = input_anf_node->cast<ParameterPtr>();
-      if (AnfAlgo::IsParameterWeight(para) && AnfAlgo::GetOutputDeviceDataType(para, 0) == kTypeUnknown) {
-        continue;
-      }
-    }
-    if (kernel_build_info.GetInputFormat(input_index) == AnfAlgo::GetPrevNodeOutputFormat(kernel_node, input_index)) {
-      (*cur_kernelinfo_match_counts)[MATCH_FORMAT_COUNT]++;
-    }
-    if (kernel_build_info.GetInputDeviceType(input_index) ==
-        AnfAlgo::GetPrevNodeOutputDeviceDataType(kernel_node, input_index)) {
-      (*cur_kernelinfo_match_counts)[MATCH_DTYPE_COUNT]++;
-    }
-    if (kernel_build_info.GetInputFormat(input_index) == kOpFormat_FRAC_NZ) {
-      (*cur_kernelinfo_match_counts)[MATCH_NZ_FORMAT_COUNT]++;
-    }
-    if (kernel_build_info.GetInputFormat(input_index) == kOpFormat_NC1HWC0) {
-      (*cur_kernelinfo_match_counts)[MATCH_5D_FORMAT_COUNT]++;
-    }
-  }
-
-  for (size_t output_index = 0; output_index < AnfAlgo::GetOutputTensorNum(kernel_node); ++output_index) {
-    // cal count of same output dtype between abstract and kernel info
-    if (kernel_build_info.GetOutputDeviceType(output_index) ==
-        AnfAlgo::GetOutputInferDataType(kernel_node, output_index)) {
-      (*cur_kernelinfo_match_counts)[MATCH_OUTPUT_DTYPE_COUNT]++;
-    }
-  }
-}
-
-void SetKernelBuildInfo(KernelBuilderPtr builder) {
-  builder->SetFusionType(kernel::OPAQUE);
-  builder->SetKernelType(AUTO_DIFF_KERNEL);
-  builder->SetProcessor(kernel::AICORE);
-}
-
-void test_select(const CNodePtr &kernel_node, std::vector<std::shared_ptr<kernel::KernelBuildInfo>> kernel_info_list) {
-  std::vector<int> most_match_counts = {-1, -1, -1, -1, -1};
-  int selected_index = -1;
-  for (size_t info_index = 0; info_index < kernel_info_list.size(); ++info_index) {
-    std::vector<int> cur_kernel_info_match_counts = {0, 0, 0, 0, 0};
-    if (!IsValidKernelInfo(kernel_node, *(kernel_info_list[info_index]))) {
-      continue;
-    }
-    if (!MatchInferOutputDataType(kernel_node, *(kernel_info_list[info_index]))) {
-      continue;
-    }
-    std::shared_ptr<kernel::KernelBuildInfo> kernel_info_ptr = kernel_info_list[info_index];
-    UpdateCurMatchCounts(*kernel_info_ptr, kernel_node, &cur_kernel_info_match_counts);
-    // Currently the selection policy is the match format count first, and then is datatype counts.
-    if (PriorityChooseItem(cur_kernel_info_match_counts, &most_match_counts)) {
-      selected_index = SizeToInt(info_index);
-    }
-  }
-  if (selected_index == -1) {
-    MS_EXCEPTION(NotExistsError) << "" << kernel_node->DebugString() << " Cannot find valid kernel Info !";
-  }
-  auto index = IntToSize(selected_index);
-  if (index >= kernel_info_list.size()) {
-    MS_EXCEPTION(ArgumentError) << "index outof range";
-  }
-  std::shared_ptr<kernel::KernelBuildInfo> selected_kernel_info_ptr = kernel_info_list[index];
-  MS_EXCEPTION_IF_NULL(selected_kernel_info_ptr);
-  AnfAlgo::SetSelectKernelBuildInfo(selected_kernel_info_ptr, kernel_node.get());
-}
-
-void SetParentAbstract(std::vector<AnfNodePtr> parent_list, std::vector<std::vector<size_t>> shapes,
-                       std::vector<TypeId> types) {
-  for (const auto &node : parent_list) {
-    AnfAlgo::SetOutputInferTypeAndShape(types, shapes, node.get());
-  }
-}
-}  // namespace
-class AscendKernelSelctTest : public UT::Common {
- public:
-  AscendKernelSelctTest() = default;
-  void SetUp() override {}
-  void TearDown() override {}
-};
-
-TEST_F(AscendKernelSelctTest, TestSelect) {
-  std::vector<KernelBuilderPtr> build_list;
-  std::vector<TypeId> type_list = {kNumberTypeFloat32};
-  for (size_t i = 0; i <= 4; ++i) {
-    build_list.push_back(std::make_shared<KernelBuildInfoBuilder>());
-    SetKernelBuildInfo(build_list[i]);
-    build_list[i]->SetInputsDeviceType(type_list);
-    build_list[i]->SetOutputsDeviceType(type_list);
-  }
-
-  std::vector<std::string> nd_fmt = {kOpFormat_DEFAULT};
-  std::vector<std::string> nz_fmt = {kOpFormat_FRAC_NZ};
-  auto anf_graph = std::make_shared<KernelGraph>();
-
-  // 16's multiple should not chose format NZ
-  Shape nd_shapes = {2, 32, 224, 224};
-
-  Shape nz_shapes = {3, 3, 5, 5};
-  auto add_value = NewValueNode(prim::kPrimTensorAdd);
-  auto a_node = anf_graph->NewCNode(std::vector<AnfNodePtr>{add_value});
-  auto b_node = anf_graph->NewCNode(std::vector<AnfNodePtr>{add_value});
-  std::vector<AnfNodePtr> parent_list = {add_value, a_node, b_node};
-
-  auto c_node = anf_graph->NewCNode(parent_list);
-
-  // a   b
-  //  \ /
-  //   c
-  // a & b:  kernel_info:{output_format:{nz},dtype:{kNumberTypeFloat32}}
-  //     infer_dtype:{kNumberTypeFloat32},infer_shape:{{3, 3, 5, 5}}
-  // c:  infer_dtype:{kNumberTypeFloat32},infer_shape:{{3, 3,224, 224}}
-
-  // set a & b's info
-  SetParentAbstract(parent_list, ShapeList{nz_shapes}, type_list);
-  // set abstract c
-  AnfAlgo::SetOutputInferTypeAndShape(type_list, ShapeList{nd_shapes}, c_node.get());
-  // set format of kernel info
-  build_list[0]->SetOutputsFormat(nz_fmt);
-  build_list[1]->SetOutputsFormat(nz_fmt);
-
-  build_list[2]->SetInputsFormat(std::vector<std::string>{nd_fmt[0], nd_fmt[0]});
-  build_list[3]->SetInputsFormat(std::vector<std::string>{nz_fmt[0], nz_fmt[0]});
-  build_list[2]->SetInputsDeviceType(std::vector<TypeId>{kNumberTypeFloat32, kNumberTypeFloat32});
-  build_list[3]->SetInputsDeviceType(std::vector<TypeId>{kNumberTypeFloat32, kNumberTypeFloat32});
-  build_list[2]->SetOutputsFormat(nd_fmt);
-  build_list[3]->SetOutputsFormat(nz_fmt);
-  std::vector<KernelBuildInfoPtr> select_info_list;
-  // set select info list
-  select_info_list.emplace_back(build_list[2]->Build());
-  select_info_list.emplace_back(build_list[3]->Build());
-
-  // set device info for a & b
-  AnfAlgo::SetSelectKernelBuildInfo(build_list[0]->Build(), a_node.get());
-  AnfAlgo::SetSelectKernelBuildInfo(build_list[1]->Build(), b_node.get());
-
-  test_select(c_node, select_info_list);
-  EXPECT_EQ(AnfAlgo::GetInputFormat(c_node, 0), kOpFormat_DEFAULT);
-  EXPECT_EQ(AnfAlgo::GetInputFormat(c_node, 1), kOpFormat_DEFAULT);
-
-  // set a & b's info
-  // a   b
-  //  \ /
-  //   c
-  // a: kernel_info:{output_format:{5d},dtype:{kNumberTypeFloat32}}
-  //    infer_dtype:{kNumberTypeFloat32},infer_shape:{{3, 3, 5, 5}}
-  // b:  kernel_info:{output_format:{nz},dtype:{kNumberTypeFloat32}}
-  //     infer_dtype:{kNumberTypeFloat32},infer_shape:{{3, 3, 5, 5}}
-  // c:  infer_dtype:{kNumberTypeFloat32},infer_shape:{{3, 3, 5, 5}}
-
-  // set a & b's info
-  SetParentAbstract(parent_list, ShapeList{nz_shapes}, type_list);
-  // set abstract c
-  AnfAlgo::SetOutputInferTypeAndShape(type_list, ShapeList{nz_shapes}, c_node.get());
-  // set format of kernel info
-  build_list[0]->SetOutputsFormat(std::vector<std::string>{kOpFormat_NC1HWC0});
-  build_list[1]->SetOutputsFormat(nz_fmt);
-
-  build_list[2]->SetInputsFormat(std::vector<std::string>{kOpFormat_NC1HWC0, nd_fmt[0]});
-  build_list[3]->SetInputsFormat(std::vector<std::string>{nd_fmt[0], nz_fmt[0]});
-  build_list[2]->SetInputsDeviceType(std::vector<TypeId>{kNumberTypeFloat32, kNumberTypeFloat32});
-  build_list[3]->SetInputsDeviceType(std::vector<TypeId>{kNumberTypeFloat32, kNumberTypeFloat32});
-  build_list[2]->SetOutputsFormat(nd_fmt);
-  build_list[3]->SetOutputsFormat(nz_fmt);
-  // set select info list
-  select_info_list.emplace_back(build_list[2]->Build());
-  select_info_list.emplace_back(build_list[3]->Build());
-
-  // set device info for a & b
-  AnfAlgo::SetSelectKernelBuildInfo(build_list[0]->Build(), a_node.get());
-  AnfAlgo::SetSelectKernelBuildInfo(build_list[1]->Build(), b_node.get());
-
-  test_select(c_node, select_info_list);
-  EXPECT_EQ(AnfAlgo::GetInputFormat(c_node, 0), kOpFormat_DEFAULT);
-  EXPECT_EQ(AnfAlgo::GetInputFormat(c_node, 1), kOpFormat_FRAC_NZ);
-}
-}  // namespace ascend
-}  // namespace device
-}  // namespace mindspore

tests/ut/cpp/pre_activate/ascend/ir_fission/topk_split_test.cc

@@ -39,7 +39,7 @@ class MockSupportedChecker : public SupportedChecker {
  public:
   MockSupportedChecker() = default;
   ~MockSupportedChecker() override = default;
-  bool CheckSupported(const AnfNodePtr &anf_node, const kernel::KernelBuildInfoPtr &select_kernel_build_info) override {
+  bool CheckAiCoreSupported(const AnfNodePtr &anf_node, const kernel::KernelBuildInfoPtr &select_kernel_build_info) override {
     return true;
   }
 };  // namespace opt

tests/ut/cpp/pre_activate/ascend/ir_fusion/transpose_transdata_fusion_test.cc

@@ -37,6 +37,15 @@ class TestHWTransposeTransdataFusion : public BackendCommon {
   UT::PyFuncGraphFetcher get_py_fun_;
 };
 
+class MockSupportedChecker : public SupportedChecker {
+ public:
+  MockSupportedChecker() = default;
+  ~MockSupportedChecker() override = default;
+  bool CheckAiCoreSupported(const AnfNodePtr &anf_node, const kernel::KernelBuildInfoPtr &select_kernel_build_info) override {
+    return true;
+  }
+};
+
 class MockInsertTransOpKernelSelectTrans4Dto5D : public KernelSelect {
  public:
   MockInsertTransOpKernelSelectTrans4Dto5D() = default;
@@ -60,37 +69,6 @@ class MockInsertTransOpKernelSelectTrans4Dto5D : public KernelSelect {
   }
 };
 
-class MockTransposeTransdataFusionKernelSelect : public KernelSelect {
- public:
-  MockTransposeTransdataFusionKernelSelect() = default;
-  ~MockTransposeTransdataFusionKernelSelect() override = default;
-  bool CheckKernelAccuracySupported(const CNodePtr &kernel_node,
-                                    const kernel::KernelBuildInfoPtr &new_kernel_build_info) override {
-    std::vector<std::shared_ptr<kernel::KernelBuildInfo>> kernel_info_list;
-    kernel::KernelBuildInfo::KernelBuildInfoBuilder builder;
-    builder.SetInputsFormat({kOpFormat_NCHW});
-    builder.SetOutputsFormat({kOpFormat_DEFAULT});
-    builder.SetInputsDeviceType({kNumberTypeFloat16});
-    builder.SetOutputsDeviceType({kNumberTypeFloat16});
-    builder.SetKernelType(KernelType::AUTO_DIFF_KERNEL);
-    builder.SetFusionType(kernel::FusionType::OPAQUE);
-    builder.SetProcessor(kernel::Processor::AICORE);
-    kernel_info_list.push_back(builder.Build());
-    MS_LOG(INFO) << "transpose transdata fusion success";
-    MS_LOG(INFO) << "new transdata build info input format:" << new_kernel_build_info->GetInputFormat(0)
-                 << ",outputformat:" << new_kernel_build_info->GetOutputFormat(0)
-                 << ",kerneltype:" << new_kernel_build_info->kernel_type()
-                 << ",fusiontype:" << new_kernel_build_info->fusion_type()
-                 << ",process:" << new_kernel_build_info->processor();
-    auto result = std::find_if(kernel_info_list.begin(), kernel_info_list.end(),
-                               [&new_kernel_build_info](kernel::KernelBuildInfoPtr item) {
-                                 MS_EXCEPTION_IF_NULL(item);
-                                 return *item == *new_kernel_build_info;
-                               });
-    return result != kernel_info_list.end();
-  }
-};
-
 TEST_F(TestHWTransposeTransdataFusion, test_transpose_transdata_fusion) {
   /*
    * def before(input0, input1):
@@ -128,7 +106,7 @@ TEST_F(TestHWTransposeTransdataFusion, test_transpose_transdata_fusion) {
   insert_trans_op_pass->kernel_select_ = std::make_shared<MockInsertTransOpKernelSelectTrans4Dto5D>();
   pm->AddPass(insert_trans_op_pass);
   auto transpose_transdata_pass = std::make_shared<opt::TransposeTransDataFusion>();
-  transpose_transdata_pass->kernel_select_ = std::make_shared<MockTransposeTransdataFusionKernelSelect>();
+  transpose_transdata_pass->supported_checker_ = std::make_shared<MockSupportedChecker>();
   pm->AddPass(transpose_transdata_pass);
   optimizer->AddPassManager(pm);
   FuncGraphPtr new_graph = optimizer->Optimize(kg);