gpu inceptionv3 optimize

mindspore/ccsrc/backend/kernel_compiler/gpu/math/bias_add_gpu_kernel.h

@@ -96,11 +96,13 @@ class BiasAddGpuKernel : public GpuKernel {
       b_dims[i] = (i == pos) ? SizeToInt(x_shape[i]) : 1;
     }
 
+    auto input_device_format = AnfAlgo::GetInputFormat(kernel_node, 0);
+    auto cudnn_cal_format = (input_device_format == kOpFormat_NHWC) ? CUDNN_TENSOR_NHWC : CUDNN_TENSOR_NCHW;
     CHECK_CUDNN_RET_WITH_EXCEPT(
-      cudnnSetTensorNdDescriptorEx(x_desc_, CUDNN_TENSOR_NCHW, cudnn_data_type_, SizeToInt(cudnn_dims), x_dims.get()),
+      cudnnSetTensorNdDescriptorEx(x_desc_, cudnn_cal_format, cudnn_data_type_, SizeToInt(cudnn_dims), x_dims.get()),
       "cudnnSetTensorNdDescriptor failed");
     CHECK_CUDNN_RET_WITH_EXCEPT(
-      cudnnSetTensorNdDescriptorEx(b_desc_, CUDNN_TENSOR_NCHW, cudnn_data_type_, SizeToInt(cudnn_dims), b_dims.get()),
+      cudnnSetTensorNdDescriptorEx(b_desc_, cudnn_cal_format, cudnn_data_type_, SizeToInt(cudnn_dims), b_dims.get()),
       "cudnnSetTensorNdDescriptor failed");
     CHECK_CUDNN_RET_WITH_EXCEPT(
       cudnnSetOpTensorDescriptor(op_desc_, CUDNN_OP_TENSOR_ADD, CUDNN_DATA_FLOAT, CUDNN_NOT_PROPAGATE_NAN),

mindspore/ccsrc/backend/kernel_compiler/gpu/nn/bias_add_grad_gpu_kenel.h

@@ -94,11 +94,13 @@ class BiasAddGradGpuKernel : public GpuKernel {
       }
     }
 
+    auto input_device_format = AnfAlgo::GetInputFormat(kernel_node, 0);
+    auto cudnn_cal_format = (input_device_format == kOpFormat_NHWC) ? CUDNN_TENSOR_NHWC : CUDNN_TENSOR_NCHW;
     CHECK_CUDNN_RET_WITH_EXCEPT(
-      cudnnSetTensorNdDescriptorEx(dy_desc_, CUDNN_TENSOR_NCHW, cudnn_data_type_, SizeToInt(cudnn_dims), dy_dims.get()),
+      cudnnSetTensorNdDescriptorEx(dy_desc_, cudnn_cal_format, cudnn_data_type_, SizeToInt(cudnn_dims), dy_dims.get()),
       "cudnnSetTensorNdDescriptor failed");
     CHECK_CUDNN_RET_WITH_EXCEPT(
-      cudnnSetTensorNdDescriptorEx(db_desc_, CUDNN_TENSOR_NCHW, cudnn_data_type_, SizeToInt(cudnn_dims), db_dims.get()),
+      cudnnSetTensorNdDescriptorEx(db_desc_, cudnn_cal_format, cudnn_data_type_, SizeToInt(cudnn_dims), db_dims.get()),
       "cudnnSetTensorNdDescriptor failed");
     CHECK_CUDNN_RET_WITH_EXCEPT(
       cudnnSetReduceTensorDescriptor(op_desc_, CUDNN_REDUCE_TENSOR_ADD, CUDNN_DATA_FLOAT, CUDNN_NOT_PROPAGATE_NAN,

mindspore/ccsrc/backend/optimizer/gpu/insert_format_transform_op.cc

@@ -18,6 +18,7 @@
 #include <memory>
 #include <vector>
 #include <string>
+#include <algorithm>
 #include "backend/session/anf_runtime_algorithm.h"
 #include "ir/primitive.h"
 #include "utils/utils.h"
@@ -38,21 +39,22 @@ std::vector<int> TransposeAxis(const std::string &src_format, const std::string
 }
 
 // Transpose can be replaceed by nop reshape in some situations.
-// 1. out_shape [x, 1, 1, y] with transpose perm {0, 2, 3, 1}
-// 2. out_shape [x, y, 1, 1] with transpose perm {0, 3, 1, 2}
+// 1. out_shape [x, 1, 1, y]
+// 2. out_shape [x, y, 1, 1]
+// 3. out_shape [x, 1, y, 1]
 bool IsFakeTranspose(const std::vector<size_t> &out_shape, const std::vector<int> &transpose_perm) {
   if (out_shape.size() != 4) {
     MS_LOG(EXCEPTION) << "Invalid data shape, 4-D data was needed, but get " << out_shape.size() << "-D.";
   }
   std::vector<int> perm1 = {0, 2, 3, 1};
   std::vector<int> perm2 = {0, 3, 1, 2};
-  if (transpose_perm == perm1) {
-    return (out_shape[1] == 1 && out_shape[2] == 1);
-  } else if (transpose_perm == perm2) {
-    return (out_shape[2] == 1 && out_shape[3] == 1);
-  } else {
-    return false;
+  auto num = std::count(out_shape.begin(), out_shape.end(), 1);
+  if ((transpose_perm == perm1) || (transpose_perm == perm2)) {
+    if (num >= 2) {
+      return true;
+    }
   }
+  return false;
 }
 
 void SetTransposeOpBuildInfo(const std::string &input_format, const std::string &output_format,
@@ -73,6 +75,8 @@ void SetTransposeOpBuildInfo(const std::string &input_format, const std::string
 // Insert transpose op between node and used_node whose position is used_node_index.
 CNodePtr InsertTransposeOp(const FuncGraphPtr &graph, const AnfNodePtr &node, const AnfNodePtr &used_node,
                            int used_node_index, const std::vector<int> &transpose_perm) {
+  MS_LOG(DEBUG) << "Node: " << node->fullname_with_scope() << ", used node: " << used_node->fullname_with_scope()
+                << ", index: " << used_node_index;
   MS_EXCEPTION_IF_NULL(graph);
   // 0.Judge whether it is a fake transpose
   auto transed_shape = AnfAlgo::GetInputDeviceShape(used_node, used_node_index);
@@ -95,15 +99,10 @@ CNodePtr InsertTransposeOp(const FuncGraphPtr &graph, const AnfNodePtr &node, co
   if (!is_fake) {
     AnfAlgo::SetNodeAttr(kAttrPerm, MakeValue(transpose_perm), transpose_op);
   }
-  // 4.Set the input of used_node.
-  MS_LOG(DEBUG) << "Node: " << node->fullname_with_scope() << ", used node: " << used_node->fullname_with_scope()
-                << ", index: " << used_node_index;
-  AnfAlgo::SetNodeInput(utils::cast<CNodePtr>(used_node), transpose_op, used_node_index);
-  // 5. Update the manager info of transpose op.
+  // 4. Set the new edge of transpose op.
   FuncGraphManagerPtr manager = graph->manager();
   MS_EXCEPTION_IF_NULL(manager);
-  manager->Clear();
-  manager->AddFuncGraph(graph);
+  manager->SetEdge(used_node, used_node_index + 1, transpose_op);
   return transpose_op;
 }
 }  // namespace

mindspore/ccsrc/runtime/device/gpu/kernel_info_setter.cc

@@ -252,11 +252,11 @@ void FormatTransformChecker::CheckSupportFormatTransform(const std::shared_ptr<s
       bn_cnt++;
     }
   }
-  if (conv_cnt == kConv2dCount && bn_cnt == kFusedBatchNormCount) {
-    format_transform_ = false;
+  if (conv_cnt + bn_cnt > 1) {
+    format_transform_ = true;
     return;
   }
-  format_transform_ = true;
+  format_transform_ = false;
 }
 
 void SetKernelInfo(const CNodePtr &kernel_node) {

mindspore/ccsrc/runtime/device/gpu/kernel_info_setter.h

@@ -34,23 +34,27 @@ namespace gpu {
 // map<opName, (inputFormatPosition, outputFormatPosition)>, used for getting the insert position of format transform.
 // If input position is empty, then insert all the input positions, because the input numbers of this op are variable.
 static std::map<std::string, std::pair<std::vector<size_t>, std::vector<size_t>>> kKernelFormatPositionMap = {
+  // Format sensitive.
   {prim::kPrimConv2D->name(), {{0, 1}, {0}}},
   {prim::kPrimConv2DBackpropInput->name(), {{0, 1}, {0}}},
   {prim::kPrimConv2DBackpropFilter->name(), {{0, 1}, {0}}},
-  {prim::kPrimRelu->name(), {{0}, {0}}},
-  {prim::kPrimReluGrad->name(), {{0, 1}, {0}}},
   {prim::kPrimMaxPool->name(), {{0}, {0}}},
   {prim::kPrimMaxPoolGrad->name(), {{0, 1, 2}, {0}}},
-  {kSliceOpName, {{0}, {0}}},
   {kAvgPoolOpName, {{0}, {0}}},
   {kAvgPoolGradGpuOpName, {{0, 1, 2}, {0}}},
-  {kTensorAddOpName, {{0, 1}, {0}}},
   {kFusedBatchNormEx, {{0}, {0}}},
   {kFusedBatchNormExWithActivation, {{0}, {0}}},
   {kFusedBatchNormExWithAddAndActivation, {{0, 5}, {0}}},
   {kFusedBatchNormGradEx, {{0, 1}, {0}}},
   {kFusedBatchNormGradExWithActivation, {{0, 1, 7}, {0}}},
   {kFusedBatchNormGradExWithAddAndActivation, {{0, 1, 7}, {0, 3}}},
+  {kBiasAddOpName, {{0}, {0}}},
+  {prim::kPrimBiasAddGrad->name(), {{0}, {}}},
+  // Format insensitive.
+  {prim::kPrimRelu->name(), {{0}, {0}}},
+  {prim::kPrimReluGrad->name(), {{0, 1}, {0}}},
+  {kSliceOpName, {{0}, {0}}},
+  {kTensorAddOpName, {{0, 1}, {0}}},
   {prim::kPrimConcat->name(), {{}, {0}}},
   {prim::kPrimAddN->name(), {{}, {0}}},
 };
@@ -74,8 +78,6 @@ class FormatTransformChecker {
   FormatTransformChecker &operator=(const FormatTransformChecker &);
 
   bool format_transform_{true};
-  static constexpr size_t kConv2dCount = 96;
-  static constexpr size_t kFusedBatchNormCount = 94;
 };
 
 class KernelAttr {