!624 modify buffer fusion used num

Merge pull request !624 from jjfeing/master

mindspore/_extends/parallel_compile/tbe_compiler/common.py

@@ -122,10 +122,12 @@ def get_args(op_info, arg_type):
 
     elif arg_type == 'attrs':
         for item in op_info[arg_type]:
-            if 'value' not in item:
-                raise ValueError("Json string Errors, attr key:value not found.")
-            if item["name"] != "isRef":
-                args.append(item['value'])
+            if item["valid"]:
+                if 'value' not in item:
+                    raise ValueError("Json string Errors, attr key:value not found.")
+                if item["name"] != "isRef":
+                    args.append(item['value'])
+
     return args
 
 

mindspore/ccsrc/kernel/kernel_fusion.cc

@@ -108,7 +108,8 @@ std::map<int32_t, KernelModPtr> KernelFusion(const std::vector<FusionScopeInfo>
     }
 
     if ((task_result != nullptr) && (strcmp(task_result, "Success") != 0)) {
-      MS_LOG(DEBUG) << "fuison op build failed, err log: " << task_result << "  change to single op build.";
+      MS_LOG(INFO) << "Fusion warning: Fuison op build failed, err log: " << task_result
+                   << "  change to single op build.";
       build_failed_num++;
     }
     auto kernel_mod_item = build_manger->TaskFinishProcess(task_id, false);

mindspore/ccsrc/kernel/tbe/tbe_adapter.cc

@@ -154,6 +154,52 @@ void TbeAdapter::InputOrderPass(const std::string &op_name, std::vector<std::vec
   }
 }
 
+void TbeAdapter::FusionInputOrderPass(const std::string &op_name, const std::vector<nlohmann::json> &inputs_list,
+                                      std::vector<nlohmann::json> *inputs_json) {
+  MS_EXCEPTION_IF_NULL(inputs_json);
+  if (input_order_adjusted_ops.find(op_name) == input_order_adjusted_ops.end()) {
+    (void)std::copy(inputs_list.begin(), inputs_list.end(), std::back_inserter((*inputs_json)));
+  } else {
+    if (op_name == "MinimumGrad" || op_name == "MaximumGrad") {
+      inputs_json->emplace_back(inputs_list[2]);
+      inputs_json->emplace_back(inputs_list[0]);
+      inputs_json->emplace_back(inputs_list[1]);
+      for (size_t i = 3; i < inputs_list.size(); ++i) {
+        inputs_json->emplace_back(inputs_list[i]);
+      }
+    } else {
+      inputs_json->emplace_back(inputs_list[1]);
+      inputs_json->emplace_back(inputs_list[0]);
+      for (size_t i = 2; i < inputs_list.size(); ++i) {
+        inputs_json->emplace_back(inputs_list[i]);
+      }
+    }
+  }
+}
+
+void TbeAdapter::FusionDataOrderPass(const std::string &op_name, const std::vector<AnfNodePtr> &data_layer,
+                                     std::vector<AnfNodePtr> *reorder_data_layer) {
+  MS_EXCEPTION_IF_NULL(reorder_data_layer);
+  if (input_order_adjusted_ops.find(op_name) == input_order_adjusted_ops.end()) {
+    (void)std::copy(data_layer.begin(), data_layer.end(), std::back_inserter((*reorder_data_layer)));
+  } else {
+    if (op_name == "MinimumGrad" || op_name == "MaximumGrad") {
+      reorder_data_layer->emplace_back(data_layer[2]);
+      reorder_data_layer->emplace_back(data_layer[0]);
+      reorder_data_layer->emplace_back(data_layer[1]);
+      for (size_t i = 3; i < data_layer.size(); ++i) {
+        reorder_data_layer->emplace_back(data_layer[i]);
+      }
+    } else {
+      reorder_data_layer->emplace_back(data_layer[1]);
+      reorder_data_layer->emplace_back(data_layer[0]);
+      for (size_t i = 2; i < data_layer.size(); ++i) {
+        reorder_data_layer->emplace_back(data_layer[i]);
+      }
+    }
+  }
+}
+
 std::map<std::string, FAttrsPass> TbeAdapter::build_json_attr_pass_map_ = {
   {"MaximumGrad", TbeAdapter::MaximumGradAttrJsonPass},
   {"MinimumGrad", TbeAdapter::MinimumGradAttrJsonPass},

mindspore/ccsrc/kernel/tbe/tbe_adapter.h

@@ -44,15 +44,12 @@ class TbeAdapter {
   static void GenTopKV2IndicesTensorInfo(const std::shared_ptr<AnfNode> &anf_node, size_t real_input_index,
                                          std::vector<nlohmann::json> *input_list, kCreaterType creater_type);
 
+  static void FusionInputOrderPass(const std::string &op_name, const std::vector<nlohmann::json> &inputs_list,
+                                   std::vector<nlohmann::json> *inputs_json);
+  static void FusionDataOrderPass(const std::string &op_name, const std::vector<AnfNodePtr> &data_layer,
+                                  std::vector<AnfNodePtr> *reorder_data_layer);
+
  private:
-  static void Conv2DAttrJsonPass(const AnfNodePtr &anf_node, const std::vector<std::shared_ptr<OpAttr>> &op_info_attrs,
-                                 nlohmann::json *attrs_json);
-  static void Conv2DBackpropFilterAttrJsonPass(const AnfNodePtr &anf_node,
-                                               const std::vector<std::shared_ptr<OpAttr>> &op_info_attrs,
-                                               nlohmann::json *attrs_json);
-  static void Conv2DBackpropInputAttrJsonPass(const AnfNodePtr &anf_node,
-                                              const std::vector<std::shared_ptr<OpAttr>> &op_info_attrs,
-                                              nlohmann::json *attrs_json);
   static void MaximumGradAttrJsonPass(const AnfNodePtr &anf_node,
                                       const std::vector<std::shared_ptr<OpAttr>> &op_info_attrs,
                                       nlohmann::json *attrs_json);

mindspore/ccsrc/kernel/tbe/tbe_kernel_build.cc

@@ -375,20 +375,26 @@ bool TbeKernelJsonCreator::GenTbeAttrJson(const std::shared_ptr<AnfNode> &anf_no
   MS_EXCEPTION_IF_NULL(primitive);
   for (const auto &attr_ptr : attrs_ptr) {
     std::string attr_name = attr_ptr->name();
+    nlohmann::json attr_obj;
+    attr_obj["name"] = attr_name;
     if (primitive->GetAttr(attr_name) != nullptr) {
-      nlohmann::json attr_obj;
       auto value = primitive->GetAttr(attr_name);
       std::string type = attr_ptr->type();
       ParseAttrValue(type, value, &attr_obj);
-      attr_obj["name"] = attr_name;
       attr_obj["valid"] = true;
-      (*attrs_json).push_back(attr_obj);
     } else {
-      if (attr_ptr->param_type() == "required" && creater_type_ == SINGLE_BUILD && op_info->impl_path() != "") {
-        MS_LOG(EXCEPTION) << "op name: " << op_info->op_name() << " attr: " << attr_name
-                          << " is required, but not set.";
+      if (op_info->impl_path().empty()) {
+        attr_obj["valid"] = false;
+      } else {
+        if (attr_ptr->param_type() == "required" && creater_type_ == SINGLE_BUILD) {
+          MS_LOG(EXCEPTION) << "op name: " << op_info->op_name() << " attr: " << attr_name
+                            << " is required, but not set.";
+        } else {
+          attr_obj["valid"] = false;
+        }
       }
     }
+    (*attrs_json).push_back(attr_obj);
   }
   return true;
 }
@@ -484,7 +490,8 @@ bool TbeKernelBuild::GenFusionScopeJson(const vector<mindspore::AnfNodePtr> &inp
   MS_EXCEPTION_IF_NULL(fusion_kernel);
   // get input layer info
   std::vector<std::vector<mindspore::AnfNodePtr>> input_layers;
-  if (!GetInputLayers(input_nodes, compute_nodes, &input_layers)) {
+  std::map<const AnfNodePtr, FusionDataType> spec_data_input;
+  if (!GetInputLayers(input_nodes, compute_nodes, &input_layers, &spec_data_input)) {
     return false;
   }
   // gen fusion scopre_op jsom
@@ -505,8 +512,8 @@ bool TbeKernelBuild::GenFusionScopeJson(const vector<mindspore::AnfNodePtr> &inp
   for (const auto &layer : input_layers) {
     for (const auto &data_input : layer) {
       nlohmann::json data_str;
-      if (!GenFusionDataInputJson(data_input, &data_str, &index)) {
-        MS_LOG(DEBUG) << "GenFusionDataInputJson faild.";
+      if (!GenFusionDataInputJson(data_input, spec_data_input, &data_str, &index)) {
+        MS_LOG(INFO) << "Fusion error: gen fusion datainput json faild.";
         return false;
       }
       data_list.push_back(data_str);
@@ -519,7 +526,7 @@ bool TbeKernelBuild::GenFusionScopeJson(const vector<mindspore::AnfNodePtr> &inp
 }
 
 void TbeKernelBuild::GenDescJson(const std::shared_ptr<mindspore::AnfNode> &anf_node, size_t node_out_idx,
-                                 size_t desc_output_idx, nlohmann::json *output_desc) {
+                                 size_t desc_output_idx, nlohmann::json *output_desc, FusionDataType fusion_data_type) {
   std::string output_desc_name = anf_node->fullname_with_scope();
   if (node_out_idx > 0) {
     output_desc_name = output_desc_name + "_" + std::to_string(node_out_idx);
@@ -539,58 +546,109 @@ void TbeKernelBuild::GenDescJson(const std::shared_ptr<mindspore::AnfNode> &anf_
   (*output_desc)["shape"] = shape;
   auto format = AnfAlgo::GetOutputFormat(anf_node, node_out_idx);
   if (format == kOpFormat_DEFAULT) {
-    if (ori_shape.size() == 4) {
-      format = kOpFormat_NCHW;
-    } else {
-      format = kOpFormat_ND;
-    }
+    format = ori_shape.size() == 4 ? kOpFormat_NCHW : kOpFormat_ND;
   }
   (*output_desc)["format"] = format;
   (*output_desc)["ori_format"] = kOpFormat_NCHW;
   (*output_desc)["output_index"] = desc_output_idx;
+  if (fusion_data_type == kFusionAddN && format == kOpFormat_NC1HWC0) {
+    std::vector<size_t> spec_shape = {};
+    spec_shape.emplace_back(shape[0]);
+    spec_shape.emplace_back(shape[1]);
+    spec_shape.emplace_back(shape[2] * shape[3]);
+    spec_shape.emplace_back(shape[4]);
+    (*output_desc)["shape"] = spec_shape;
+  } else if (fusion_data_type == kFusionReLUGradV2 && (*output_desc)["data_type"] == "uint8") {
+    std::vector<size_t> spec_shape = {};
+    spec_shape.emplace_back(shape[0]);
+    spec_shape.emplace_back(shape[1]);
+    spec_shape.emplace_back(shape[2] * shape[3]);
+    spec_shape.emplace_back(16);
+    (*output_desc)["shape"] = spec_shape;
+    (*output_desc)["data_type"] = "bool";
+  }
 }
 
 void TbeKernelBuild::GenReusedOutputDesc(const shared_ptr<mindspore::AnfNode> &anf_node, size_t index,
                                          size_t output_index, nlohmann::json *output_desc) {
   std::string output_desc_name = anf_node->fullname_with_scope() + "_" + std::to_string(index);
   (*output_desc)["name"] = NormalizeFullScopeName(output_desc_name);
-  (*output_desc)["data_type"] = tbe::TypeIdToString(kNumberTypeFloat32);
   (*output_desc)["output_index"] = output_index;
   std::vector<size_t> shape;
   (*output_desc)["shape"] = shape;
 }
 
-bool TbeKernelBuild::GetInputLayers(const vector<mindspore::AnfNodePtr> &input_nodes,
-                                    const vector<mindspore::AnfNodePtr> &compute_nodes,
-                                    std::vector<std::vector<mindspore::AnfNodePtr>> *input_layers) {
+bool TbeKernelBuild::GetSpecInputLayers(const std::string &op_name,
+                                        const std::vector<mindspore::AnfNodePtr> &reorder_layer,
+                                        std::map<const AnfNodePtr, FusionDataType> *spec_data_input) {
+  if ((op_name == kReluGradV2OpName || op_name == kAddNOpName) && reorder_layer.empty()) {
+    MS_LOG(INFO) << "Fusion error: node(" << op_name << " )'s input is null. ";
+    return false;
+  }
+  MS_LOG(INFO) << "Fusion info: op_name: " << op_name << "input layer size: " << reorder_layer.size();
+  if (op_name == kReluGradV2OpName) {
+    (*spec_data_input)[reorder_layer[0]] = kFusionReLUGradV2;
+  } else if (op_name == kAddNOpName) {
+    for (const auto &it : reorder_layer) {
+      (*spec_data_input)[it] = kFusionAddN;
+    }
+  }
+  return true;
+}
+
+bool TbeKernelBuild::GetInputLayers(const std::vector<mindspore::AnfNodePtr> &input_nodes,
+                                    const std::vector<mindspore::AnfNodePtr> &compute_nodes,
+                                    std::vector<std::vector<mindspore::AnfNodePtr>> *input_layers,
+                                    std::map<const AnfNodePtr, FusionDataType> *spec_data_input) {
+  auto result = std::find_if(compute_nodes.begin(), compute_nodes.end(), [](const auto &it) {
+    auto op_name = AnfAlgo::GetCNodeName(it);
+    return op_name == kConv2DBackpropInputOpName;
+  });
+  bool need_spec = (result != compute_nodes.end());
   size_t input_size = 0;
   for (const auto &compute_node : compute_nodes) {
-    std::vector<mindspore::AnfNodePtr> layer;
+    std::vector<mindspore::AnfNodePtr> layer = {};
+    std::vector<mindspore::AnfNodePtr> reorder_layer = {};
     MS_EXCEPTION_IF_NULL(compute_node);
+    auto op_name = AnfAlgo::GetCNodeName(compute_node);
     auto ccompute_node = compute_node->cast<CNodePtr>();
     if (ccompute_node == nullptr) {
-      MS_LOG(DEBUG) << "fusion compute node must be cnode";
+      MS_LOG(INFO) << "Fusion error: fusion compute node must be cnode";
       return false;
     }
+    MS_LOG(INFO) << "Fusion info: compute name: " << compute_node->fullname_with_scope();
     for (size_t i = 1; i < ccompute_node->inputs().size(); ++i) {
       auto input = ccompute_node->input(i);
       auto find_iter = std::find(input_nodes.begin(), input_nodes.end(), input);
       if (find_iter != input_nodes.end()) {
+        MS_LOG(INFO) << "Fusion info: add compute node's [" << i << "] input: " << input->fullname_with_scope();
         layer.emplace_back((*find_iter));
+      } else {
+        MS_LOG(INFO) << "Fusion warnig: this input [" << i << "] may be pre compute(" << input->fullname_with_scope()
+                     << ") node's output.";
+      }
+    }
+    TbeAdapter::FusionDataOrderPass(op_name, layer, &reorder_layer);
+    if (need_spec) {
+      MS_LOG(INFO) << "Fusion info: match conv2d backprop input + ... patten.";
+      if (!GetSpecInputLayers(op_name, reorder_layer, spec_data_input)) {
+        return false;
       }
     }
-    input_size += layer.size();
-    input_layers->emplace_back(layer);
+    input_size += reorder_layer.size();
+    input_layers->emplace_back(reorder_layer);
   }
   if (input_nodes.size() != input_size) {
-    MS_LOG(DEBUG) << "fusion scope error, layer input:" << input_size << ", input_node:" << input_nodes.size();
+    MS_LOG(INFO) << "Fusion error: fusion scope error, layer input:" << input_size
+                 << ", input_node:" << input_nodes.size();
     return false;
   }
   return true;
 }
 
-bool TbeKernelBuild::GenFusionDataInputJson(const shared_ptr<mindspore::AnfNode> &data_input, nlohmann::json *data_str,
-                                            size_t *index) {
+bool TbeKernelBuild::GenFusionDataInputJson(const std::shared_ptr<mindspore::AnfNode> &data_input,
+                                            const std::map<const AnfNodePtr, FusionDataType> &spec_data_input,
+                                            nlohmann::json *data_str, size_t *index) {
   MS_EXCEPTION_IF_NULL(data_str);
   MS_EXCEPTION_IF_NULL(index);
   std::vector<nlohmann::json> output_desc_list;
@@ -604,13 +662,17 @@ bool TbeKernelBuild::GenFusionDataInputJson(const shared_ptr<mindspore::AnfNode>
     output_desc_list.push_back(output_desc);
     (*index)++;
   } else {
+    FusionDataType fusion_data_type = kFusionNormal;
+    if (spec_data_input.find(data_input) != spec_data_input.end()) {
+      fusion_data_type = spec_data_input.at(data_input);
+    }
     auto kernel_idx = AnfAlgo::VisitKernel(data_input, 0);
     auto real_node = kernel_idx.first;
     size_t real_idx = kernel_idx.second;
     MS_LOG(INFO) << "real name " << real_node->fullname_with_scope() << " index:" << real_idx;
     // "output_desc"
     nlohmann::json output_desc;
-    GenDescJson(real_node, real_idx, real_idx, &output_desc);
+    GenDescJson(real_node, real_idx, real_idx, &output_desc, fusion_data_type);
     output_desc_list.push_back(output_desc);
     (*data_str)["name"] = NormalizeFullScopeName(real_node->fullname_with_scope());
   }
@@ -632,11 +694,12 @@ bool TbeKernelBuild::IsDynamicInput(const mindspore::CNodePtr &cnode) {
     auto real_input_size = cnode->inputs().size() - 1;
     auto dyn_input_size = dyn_input_sizes.size();
     if (dyn_input_size != 1) {
-      MS_LOG(DEBUG) << "fusion build not support dyn_input_sizes > 1";
+      MS_LOG(INFO) << "Fusion error: fusion build not support dyn_input_sizes > 1";
       return ret;
     }
     if (IntToSize(dyn_input_sizes[0]) != real_input_size) {
-      MS_LOG(DEBUG) << " dyn_input_size" << dyn_input_sizes[0] << "not equal real_input_size" << real_input_size;
+      MS_LOG(INFO) << "Fusion error: dyn_input_size" << dyn_input_sizes[0] << "not equal real_input_size"
+                   << real_input_size;
       return ret;
     }
     ret = true;
@@ -663,6 +726,7 @@ bool TbeKernelBuild::GenFusionComputeInputJson(const mindspore::CNodePtr &cnode,
                                                std::vector<nlohmann::json> *input_desc_list, size_t *index) {
   MS_EXCEPTION_IF_NULL(cnode);
   MS_EXCEPTION_IF_NULL(input_desc_list);
+  std::vector<nlohmann::json> input_desc_list_tmp = {};
   bool is_dynamic_input = IsDynamicInput(cnode);
   for (size_t i = 1; i < cnode->inputs().size(); ++i) {
     auto input = cnode->input(i);
@@ -676,7 +740,7 @@ bool TbeKernelBuild::GenFusionComputeInputJson(const mindspore::CNodePtr &cnode,
       MS_LOG(INFO) << "node has dynamic input.";
       input_desc["dyn_index"] = (i - 1);
     }
-    (*input_desc_list).emplace_back(input_desc);
+    input_desc_list_tmp.emplace_back(input_desc);
   }
   size_t optional_num = GetOptionalInput(cnode, is_dynamic_input);
   if (optional_num > 0) {
@@ -686,35 +750,24 @@ bool TbeKernelBuild::GenFusionComputeInputJson(const mindspore::CNodePtr &cnode,
       optional_input_desc["name"] = std::string(kOptional) + std::to_string(*index);
       (*index)++;
       (*layer_iter)->emplace_back(nullptr);
-      (*input_desc_list).emplace_back(optional_input_desc);
+      input_desc_list_tmp.emplace_back(optional_input_desc);
     }
   }
+  auto op_name = AnfAlgo::GetCNodeName(cnode);
+  TbeAdapter::FusionInputOrderPass(op_name, input_desc_list_tmp, input_desc_list);
   return true;
 }
 
 std::vector<size_t> TbeKernelBuild::GetDescOutputIndex(const std::vector<int> &output_used_nums) {
   std::vector<size_t> desc_output_index = {};
-  bool find_reused = false;
-  size_t reused_num = 0;
   for (size_t idx = 0; idx < output_used_nums.size(); ++idx) {
     auto output_use_num_item = output_used_nums[idx];
     MS_LOG(INFO) << "output used num[" << idx << "] = " << output_use_num_item;
-    if (output_use_num_item == 1 || output_use_num_item == 0) {
+    desc_output_index.emplace_back(idx);
+    if (output_use_num_item > 1) {
       desc_output_index.emplace_back(idx);
-    } else {
-      if (!find_reused) {
-        desc_output_index.emplace_back(idx);
-      } else {
-        desc_output_index.emplace_back(desc_output_index[idx - 1]);
-      }
-      reused_num += (output_use_num_item - 1);
-      find_reused = true;
     }
   }
-  auto pad_value = output_used_nums.size() == 1 ? 0 : desc_output_index[desc_output_index.size() - 1] + 1;
-  for (size_t i = 0; i < reused_num; ++i) {
-    desc_output_index.emplace_back(pad_value);
-  }
   return desc_output_index;
 }
 
@@ -811,6 +864,7 @@ bool TbeKernelBuild::GetIOSize(const nlohmann::json &fusion_op_list, const vecto
         }
         auto ret = GetIOSizeImpl(data_output);
         input_size_list->push_back(ret);
+        MS_LOG(INFO) << "Fusion info: scope input name： " << op["name"] << ", size: " << ret;
       }
     }
   }
@@ -819,26 +873,31 @@ bool TbeKernelBuild::GetIOSize(const nlohmann::json &fusion_op_list, const vecto
     auto kernel_idx = AnfAlgo::VisitKernel(output_node, 0);
     auto real_node = kernel_idx.first;
     size_t real_idx = kernel_idx.second;
+    auto normal_name = NormalizeFullScopeName(real_node->fullname_with_scope());
+    MS_LOG(INFO) << "Fusion info: real node name: " << normal_name << ", real output index: " << real_idx;
     for (const auto &op : fusion_op_list) {
-      auto normal_name = NormalizeFullScopeName(real_node->fullname_with_scope());
       if (op["name"] == normal_name) {
         auto op_output_desces = op["output_desc"];
         if (output_node != real_node) {
           // tuple_get item
-          MS_LOG(DEBUG) << "output is a tuple getitem node";
+          MS_LOG(INFO) << "output is a tuple getitem node";
           auto output_desc = op_output_desces[real_idx];
           if (output_desc["shape"].empty()) {
-            continue;
+            MS_LOG(INFO) << "Fusion error: output_desc's shape is empty. real_index " << real_idx;
+            return false;
           }
           auto ret = GetIOSizeImpl(output_desc);
           output_size_list->push_back(ret);
+          MS_LOG(INFO) << "Fusion info: scope output index： " << real_idx << ", size: " << ret;
         } else {
           for (const auto &output_desc : op_output_desces) {
             if (output_desc["shape"].empty()) {
+              MS_LOG(INFO) << "Fusion info: output_desc's shape is empty, may be this node output";
               continue;
             }
             auto ret = GetIOSizeImpl(output_desc);
             output_size_list->push_back(ret);
+            MS_LOG(INFO) << "Fusion info: scope output size: " << ret;
           }
         }
       }

mindspore/ccsrc/kernel/tbe/tbe_kernel_build.h

@@ -35,6 +35,8 @@ namespace kernel {
 // kernel operate type used for generate json
 
 class TbeKernelBuild {
+  enum FusionDataType { kFusionNormal = 0, kFusionAddN, kFusionReLUGradV2 };
+
  public:
   static bool GetIOSize(const nlohmann::json &kernel_json, std::vector<size_t> *input_size_list,
                         std::vector<size_t> *output_size_list);
@@ -48,8 +50,9 @@ class TbeKernelBuild {
  private:
   TbeKernelBuild() = default;
   ~TbeKernelBuild() = default;
-  static bool GenFusionDataInputJson(const std::shared_ptr<mindspore::AnfNode> &data_input, nlohmann::json *data_str,
-                                     size_t *index);
+  static bool GenFusionDataInputJson(const std::shared_ptr<mindspore::AnfNode> &data_input,
+                                     const std::map<const AnfNodePtr, FusionDataType> &spec_data_input,
+                                     nlohmann::json *data_str, size_t *index);
   static bool GenFusionComputeJson(const mindspore::AnfNodePtr &compute_node,
                                    std::vector<std::vector<mindspore::AnfNodePtr>>::iterator *layer_iter,
                                    nlohmann::json *compute_op_str, std::string *fusion_kernel_name, size_t *index);
@@ -60,13 +63,17 @@ class TbeKernelBuild {
   static bool GenFusionComputeOutputJson(const mindspore::CNodePtr &cnode,
                                          std::vector<nlohmann::json> *output_desc_list);
   static void GenDescJson(const std::shared_ptr<mindspore::AnfNode> &anf_node, size_t node_out_idx,
-                          size_t desc_output_idx, nlohmann::json *output_desc);
+                          size_t desc_output_idx, nlohmann::json *output_desc,
+                          FusionDataType fusion_data_type = kFusionNormal);
   static void GenReusedOutputDesc(const std::shared_ptr<mindspore::AnfNode> &anf_node, size_t index,
                                   size_t output_index, nlohmann::json *output_desc);
   static size_t GetIOSizeImpl(const nlohmann::json &desc);
+  static bool GetSpecInputLayers(const std::string &op_name, const std::vector<mindspore::AnfNodePtr> &reorder_layer,
+                                 std::map<const AnfNodePtr, FusionDataType> *spec_data_input);
   static bool GetInputLayers(const std::vector<mindspore::AnfNodePtr> &input_nodes,
                              const std::vector<mindspore::AnfNodePtr> &compute_nodes,
-                             std::vector<std::vector<mindspore::AnfNodePtr>> *input_layers);
+                             std::vector<std::vector<mindspore::AnfNodePtr>> *input_layers,
+                             std::map<const AnfNodePtr, FusionDataType> *spec_data_input);
   static bool IsDynamicInput(const CNodePtr &cnode);
   static size_t GetOptionalInput(const CNodePtr &cnode, bool is_dynamic_input);
 };

mindspore/ccsrc/utils/utils.h

@@ -122,6 +122,8 @@ constexpr auto kSendOpName = "Send";
 constexpr auto kRecvOpName = "Recv";
 constexpr auto kReluV2OpName = "ReLUV2";
 constexpr auto kReluGradV2OpName = "ReluGradV2";
+constexpr auto kAddNOpName = "AddN";
+constexpr auto kConv2DBackpropInputOpName = "Conv2DBackpropInput";
 constexpr auto kFusionOpConv2DBackpropInputReluGradV2Name = "FusionOp_Conv2DBackpropInput_ReluGradV2";
 constexpr auto kFusionOpConv2DBackpropInputAddNReluGradV2Name = "FusionOp_Conv2DBackpropInput_AddN_ReluGradV2";
 

mindspore/ops/_op_impl/tbe/reduce_mean.py

@@ -31,6 +31,7 @@ reduce_mean_op_info = TBERegOp("ReduceMean") \
     .dtype_format(DataType.U8_Default, DataType.U8_Default) \
     .dtype_format(DataType.F16_Default, DataType.F16_Default) \
     .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
     .get_op_info()
 
 

mindspore/ops/operations/nn_ops.py

@@ -701,6 +701,7 @@ class Conv2D(PrimitiveWithInfer):
         self.add_prim_attr('data_format', "NCHW")
         self.out_channel = validator.check_integer('out_channel', out_channel, 0, Rel.GT, self.name)
         self.group = validator.check_integer('group', group, 0, Rel.GT, self.name)
+        self.add_prim_attr('offset_a', 0)
 
     def infer_shape(self, x_shape, w_shape):
         validator.check_integer("weight rank", len(w_shape), 4, Rel.EQ, self.name)