!1013 Support specifying reshape type for batchnorm fused op

Merge pull request !1013 from YuJianfeng/master

mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc

@@ -321,9 +321,11 @@ void ReplaceByDynamicFormatDtype(const CNodePtr &kernel_node, const std::shared_
     MS_LOG(INFO) << "Dynamic select format response successful, use dynamic format.";
     for (size_t i = 0; i < inputs_static.size(); i++) {
       inputs_dyn[i]->set_param_type(inputs_static[i]->param_type());
+      inputs_dyn[i]->set_reshape_type(inputs_static[i]->reshape_type());
     }
     for (size_t j = 0; j < outputs_static.size(); j++) {
       outputs_dyn[j]->set_param_type(outputs_static[j]->param_type());
+      outputs_dyn[j]->set_reshape_type(outputs_static[j]->reshape_type());
     }
     op_info_new_ptr->set_inputs_ptr(inputs_dyn);
     op_info_new_ptr->set_outputs_ptr(outputs_dyn);
@@ -335,6 +337,29 @@ void ReplaceByDynamicFormatDtype(const CNodePtr &kernel_node, const std::shared_
   op_info_new_ptr->set_fusion_type(op_info_ptr->fusion_type());
 }
 
+bool StringToAxisVector(const std::string &reshape_type_str, std::vector<Axis> *reshape_type_vec) {
+  for (const auto &c : reshape_type_str) {
+    switch (c) {
+      case 'N':
+        reshape_type_vec->push_back(kernel::N);
+        break;
+      case 'C':
+        reshape_type_vec->push_back(kernel::C);
+        break;
+      case 'H':
+        reshape_type_vec->push_back(kernel::H);
+        break;
+      case 'W':
+        reshape_type_vec->push_back(kernel::W);
+        break;
+      default:
+        MS_LOG(ERROR) << "Unknown axis " << c << "in reshape type.";
+        return false;
+    }
+  }
+  return true;
+}
+
 bool SetKernelBuilderInputInfo(const std::vector<std::shared_ptr<OpIOInfo>> &inputs, size_t real_input_num,
                                size_t builder_idex, const std::vector<int> &dyn_input_sizes,
                                const std::shared_ptr<KernelBuildInfo::KernelBuildInfoBuilder> &builder) {
@@ -347,6 +372,7 @@ bool SetKernelBuilderInputInfo(const std::vector<std::shared_ptr<OpIOInfo>> &inp
   MS_EXCEPTION_IF_NULL(inputs[0]);
   size_t kernel_info_cnt = inputs[0]->dtypes().size();
 
+  std::vector<std::vector<Axis>> reshape_types;
   for (const auto &input : inputs) {
     MS_EXCEPTION_IF_NULL(input);
     std::string param_type = input->param_type();
@@ -384,8 +410,14 @@ bool SetKernelBuilderInputInfo(const std::vector<std::shared_ptr<OpIOInfo>> &inp
         inputs_format.push_back(formats[builder_idex]);
       }
     }
+    std::vector<Axis> reshape_type;
+    if (!StringToAxisVector(input->reshape_type(), &reshape_type)) {
+      return false;
+    }
+    reshape_types.push_back(reshape_type);
   }
 
+  builder->SetInputReshapeType(reshape_types);
   builder->SetInputsDeviceType(inputs_device_type);
   builder->SetInputsFormat(inputs_format);
   return true;
@@ -403,6 +435,7 @@ bool SetKernelBuilderOutputInfo(const std::vector<std::shared_ptr<OpIOInfo>> &ou
   MS_EXCEPTION_IF_NULL(outputs[0]);
   size_t kernel_info_cnt = outputs[0]->dtypes().size();
 
+  std::vector<std::vector<Axis>> reshape_types;
   for (const auto &output : outputs) {
     MS_EXCEPTION_IF_NULL(output);
     if (output_idx >= real_output_num) {
@@ -436,8 +469,14 @@ bool SetKernelBuilderOutputInfo(const std::vector<std::shared_ptr<OpIOInfo>> &ou
       outputs_format.push_back(formats[builder_idex]);
       output_idx++;
     }
+    std::vector<Axis> reshape_type;
+    if (!StringToAxisVector(output->reshape_type(), &reshape_type)) {
+      return false;
+    }
+    reshape_types.push_back(reshape_type);
   }
 
+  builder->SetOutputReshapeType(reshape_types);
   builder->SetOutputsFormat(outputs_format);
   builder->SetOutputsDeviceType(outputs_device_type);
   return true;
@@ -515,7 +554,7 @@ bool IsShapeMatchFormat(const std::vector<size_t> &shape, const std::string &for
   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_FRAC_NZ, kOpFormat_NC1HWC0_C04, kOpFormat_FRACTAL_Z_C04};
 
   // if format is default, it remarkes support all format
   if (kOpFormatList.find(format) == kOpFormatList.end()) {
@@ -528,13 +567,13 @@ bool IsShapeMatchFormat(const std::vector<size_t> &shape, const std::string &for
   if (shape.empty()) {
     return true;
   }
-  if (shape.size() > kShapeSupportFormatMap.size()) {
+  if (shape.size() > kShape4dDims) {
     return false;
   }
-  if (format == kOpFormat_FRAC_NZ && shape.size() >= 2) {
-    return true;
+  if (format == kOpFormat_FRAC_NZ && shape.size() < 2) {
+    return false;
   }
-  return !(kShapeSupportFormatMap[shape.size() - 1].find(format) == kShapeSupportFormatMap[shape.size() - 1].end());
+  return true;
 }
 
 bool IsValidKernelInfo(const std::shared_ptr<CNode> &kernel_node, const kernel::KernelBuildInfo &kernel_build_info) {

mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc

@@ -55,12 +55,17 @@ CNodePtr NewTransOpNode(const FuncGraphPtr &func_graph, const AnfNodePtr &input,
   trans_inputs.push_back(input);
   CNodePtr trans_node = func_graph->NewCNode(trans_inputs);
   MS_EXCEPTION_IF_NULL(trans_node);
+  std::vector<kernel::Axis> padding_axis;
+  if (AnfAlgo::IsRealKernel(input)) {
+    padding_axis = AnfAlgo::GetOutputReshapeType(input, 0);
+  } else {
+    padding_axis = AnfAlgo::GetPrevNodeOutputReshapeType(input, 0);
+  }
   if (need_padding) {
     // if need padding we should set the transdata node's shape to the padding shape
-    AnfAlgo::SetOutputInferTypeAndShape(
-      {AnfAlgo::GetOutputInferDataType(input, 0)},
-      {trans::PaddingShapeTo4d(AnfAlgo::GetOutputInferShape(input, 0), AnfAlgo::GetOutputReshapeType(input, 0))},
-      trans_node.get());
+    AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(input, 0)},
+                                        {trans::PaddingShapeTo4d(AnfAlgo::GetOutputInferShape(input, 0), padding_axis)},
+                                        trans_node.get());
   } else {
     AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(input, 0)},
                                         {AnfAlgo::GetOutputInferShape(input, 0)}, trans_node.get());
@@ -194,8 +199,14 @@ AnfNodePtr AddTransOpNodeToGraph(const FuncGraphPtr &func_graph, const AnfNodePt
     MS_EXCEPTION_IF_NULL(cnode);
     input_node = AnfAlgo::GetInputNode(cnode, insert_index);
   }
-  bool need_padding = (trans::IsNeedPadding(dest_format, AnfAlgo::GetOutputInferShape(input_node, 0).size()) &&
-                       op_name == kTransDataOpName);
+  bool need_padding = false;
+  if (is_insert_input) {
+    need_padding = (trans::IsNeedPadding(dest_format, AnfAlgo::GetOutputInferShape(input_node, 0).size()) &&
+                    op_name == kTransDataOpName);
+  } else {
+    need_padding = (trans::IsNeedPadding(origin_format, AnfAlgo::GetOutputInferShape(input_node, 0).size()) &&
+                    op_name == kTransDataOpName);
+  }
   if (!need_padding) {
     // don't need padding insert transdata only
     trans_data = NewTransOpNode(func_graph, input_node, kernel_select, need_padding, op_name);

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

@@ -86,7 +86,6 @@ void CreateOutputsOfReduceGrad(const FuncGraphPtr &graph, const CNodePtr &bn_gra
   AnfAlgo::CopyNodeAttr(kAttrEpsilon, bn_grad_node, bn_reduce_grad);
   (*bn_reduce_grad_outputs).push_back(bn_reduce_grad);
 }
-
 }  // namespace
 const BaseRef BatchNormGradSplit::DefinePattern() const {
   VarPtr Xs = std::make_shared<SeqVar>();

mindspore/ccsrc/pre_activate/common/helper.cc

@@ -344,7 +344,7 @@ bool IsNopNode(const AnfNodePtr &node) {
   return true;
 }
 
-bool IsAllNopNode(session::KernelGraph *const graph) {
+bool IsAllNopNode(const session::KernelGraph *const graph) {
   MS_EXCEPTION_IF_NULL(graph);
   auto execution_order = graph->execution_order();
   for (auto &cnode : execution_order) {

mindspore/ccsrc/session/anf_runtime_algorithm.cc

@@ -347,6 +347,11 @@ std::string AnfRuntimeAlgorithm::GetPrevNodeOutputFormat(const AnfNodePtr &anf_n
   return AnfRuntimeAlgorithm::GetOutputFormat(kernel_with_index.first, kernel_with_index.second);
 }
 
+std::vector<kernel::Axis> AnfRuntimeAlgorithm::GetPrevNodeOutputReshapeType(const AnfNodePtr &node, size_t input_idx) {
+  KernelWithIndex kernel_with_index = AnfAlgo::GetPrevNodeOutput(node, input_idx);
+  return GetOutputReshapeType(kernel_with_index.first, kernel_with_index.second);
+}
+
 std::vector<size_t> AnfRuntimeAlgorithm::GetOutputInferShape(const AnfNodePtr &node, size_t output_idx) {
   MS_EXCEPTION_IF_NULL(node);
   abstract::BaseShapePtr base_shape = node->Shape();

mindspore/ccsrc/session/anf_runtime_algorithm.h

@@ -95,6 +95,8 @@ class AnfRuntimeAlgorithm {
   static KernelWithIndex GetPrevNodeOutput(const AnfNodePtr &anf_node, size_t input_idx);
   // get output format from prev node,input_index is the input index of current node related to prev node
   static std::string GetPrevNodeOutputFormat(const AnfNodePtr &node, size_t input_idx);
+  // get reshape_type of from the output of input node.
+  static std::vector<kernel::Axis> GetPrevNodeOutputReshapeType(const AnfNodePtr &node, size_t input_idx);
   // get output shapes inferred by ME from input nodes.
   static std::vector<size_t> GetOutputInferShape(const AnfNodePtr &node, size_t output_idx);
   // get input shapes inferred by ME from input nodes.

mindspore/ccsrc/utils/utils.h

@@ -204,6 +204,7 @@ constexpr auto kOpFormat_FRAC_Z = "FracZ";
 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};
@@ -225,8 +226,9 @@ const std::set<std::string> kOptOperatorSet = {
   kApplyRMSPropOpName,
 };
 
-const std::set<std::string> kNeedTransFormatSet = {kOpFormat_FRAC_Z, kOpFormat_NC1KHKWHWC0, kOpFormat_NC1HWC0,
-                                                   kOpFormat_FRAC_NZ, kOpFormat_C1HWNCoC0};
+const std::set<std::string> kNeedTransFormatSet = {kOpFormat_FRAC_Z,       kOpFormat_NC1KHKWHWC0, kOpFormat_NC1HWC0,
+                                                   kOpFormat_FRAC_NZ,      kOpFormat_C1HWNCoC0,   kOpFormat_NC1HWC0_C04,
+                                                   kOpFormat_FRACTAL_Z_C04};
 
 static inline void ChangeFileMode(const std::string &file_name, mode_t mode) {
   if (access(file_name.c_str(), F_OK) != 0) {

mindspore/ops/_op_impl/tbe/bn_training_reduce.py

@@ -23,7 +23,7 @@ bn_training_reduce_op_info = TBERegOp("BNTrainingReduce") \
     .compute_cost(10) \
     .kernel_name("bn_training_reduce") \
     .partial_flag(True) \
-    .input(0, "x", False, "required", "all") \
+    .input(0, "x", False, "required", "all", reshape_type="NC") \
     .output(0, "sum", False, "required", "all") \
     .output(1, "square_sum", False, "required", "all") \
     .dtype_format(DataType.F16_5HD, DataType.F32_5HD, DataType.F32_5HD) \

mindspore/ops/_op_impl/tbe/bn_training_reduce_grad.py

@@ -24,14 +24,14 @@ bn_training_reduce_grad_op_info = TBERegOp("BNTrainingReduceGrad") \
     .kernel_name("bn_training_reduce_grad") \
     .partial_flag(True) \
     .attr("epsilon", "optional", "float", "all") \
-    .input(0, "grads", False, "required", "all") \
-    .input(1, "x_norm", False, "required", "all") \
+    .input(0, "grads", False, "required", "all", reshape_type="NC") \
+    .input(1, "x_norm", False, "required", "all", reshape_type="NC") \
     .input(2, "diff_scale", False, "required", "all") \
     .input(3, "diff_offset", False, "required", "all") \
     .input(4, "scale", False, "required", "all") \
     .input(5, "batch_mean", False, "required", "all") \
     .input(6, "batch_variance", False, "required", "all") \
-    .output(0, "y", False, "required", "all") \
+    .output(0, "y", False, "required", "all", reshape_type="NC") \
     .dtype_format(DataType.F16_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
                   DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F16_5HD) \
     .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,

mindspore/ops/_op_impl/tbe/bn_training_update.py

@@ -26,14 +26,14 @@ bn_training_update_op_info = TBERegOp("BNTrainingUpdate") \
     .attr("factor", "optional", "float", "all") \
     .attr("epsilon", "optional", "float", "all") \
     .attr("isRef", "optional", "bool", "all", "true") \
-    .input(0, "x", False, "required", "all") \
+    .input(0, "x", False, "required", "all", reshape_type="NC") \
     .input(1, "sum", False, "required", "all") \
     .input(2, "square_sum", False, "required", "all") \
     .input(3, "scale", False, "required", "all") \
     .input(4, "offset", False, "required", "all") \
     .input(5, "mean", False, "required", "all") \
     .input(6, "variance", False, "required", "all") \
-    .output(0, "y", False, "required", "all") \
+    .output(0, "y", False, "required", "all", reshape_type="NC") \
     .output(1, "mean", False, "required", "all") \
     .output(2, "variance", False, "required", "all") \
     .output(3, "batch_mean", False, "required", "all") \

mindspore/ops/_op_impl/tbe/bn_training_update_grad.py

@@ -24,8 +24,8 @@ bn_training_update_grad_op_info = TBERegOp("BNTrainingUpdateGrad") \
     .kernel_name("bn_training_update_grad") \
     .partial_flag(True) \
     .attr("epsilon", "optional", "float", "all") \
-    .input(0, "grads", False, "required", "all") \
-    .input(1, "x", False, "required", "all") \
+    .input(0, "grads", False, "required", "all", reshape_type="NC") \
+    .input(1, "x", False, "required", "all", reshape_type="NC") \
     .input(2, "batch_mean", False, "required", "all") \
     .input(3, "batch_variance", False, "required", "all") \
     .output(0, "diff_scale", False, "required", "all") \