Revert "MKLDNN layout: Support for sum operator"

paddle/fluid/operators/parallel_do_op.cc

@@ -295,7 +295,7 @@ class ParallelDoGradOp : public framework::OperatorBase {
 
         auto sum_op = framework::OpRegistry::CreateOp(
             "sum", {{"X", {s, tmp_name}}}, {{"Out", {s}}},
-            framework::AttributeMap{{"use_mkldnn", {false}}});
+            framework::AttributeMap{});
         VLOG(10) << sum_op->DebugStringEx(sub_scopes[0]);
         sum_op->Run(*sub_scopes[0], places[0]);
         WaitOnPlace(places[0]);

paddle/fluid/operators/recurrent_op.cc

@@ -429,8 +429,7 @@ class RecurrentGradOp : public RecurrentBase {
 
           auto sum_op = framework::OpRegistry::CreateOp(
               "sum", {{"X", {pg_names[param_id], new_inside_name}}},
-              {{"Out", {pg_names[param_id]}}},
-              framework::AttributeMap{{"use_mkldnn", {false}}});
+              {{"Out", {pg_names[param_id]}}}, framework::AttributeMap{});
           sum_op->Run(cur_scope, place);
 
           cur_scope.Rename(new_inside_name, inside_grad_name);

paddle/fluid/operators/sum_op.cc

@@ -18,10 +18,6 @@ limitations under the License. */
 #include "paddle/fluid/framework/var_type_inference.h"
 #include "paddle/fluid/operators/detail/safe_ref.h"
 
-#ifdef PADDLE_WITH_MKLDNN
-#include "paddle/fluid/platform/mkldnn_helper.h"
-#endif
-
 namespace paddle {
 namespace operators {
 using framework::Tensor;
@@ -67,18 +63,6 @@ class SumOp : public framework::OperatorWithKernel {
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
     auto x_vars = ctx.MultiInputVar("X");
-
-    framework::LibraryType library{framework::LibraryType::kPlain};
-    framework::DataLayout layout{framework::DataLayout::kAnyLayout};
-
-#ifdef PADDLE_WITH_MKLDNN
-    if (library == framework::LibraryType::kPlain &&
-        platform::CanMKLDNNBeUsed(ctx)) {
-      library = framework::LibraryType::kMKLDNN;
-      layout = framework::DataLayout::kMKLDNN;
-    }
-#endif
-
     if (x_vars[0]->IsType<framework::LoDTensor>()) {
       int dtype = -1;
       for (auto& x_var : x_vars) {
@@ -96,27 +80,26 @@ class SumOp : public framework::OperatorWithKernel {
                         "Sum operator should have at least one tensor");
 
       return framework::OpKernelType(
-          static_cast<framework::proto::VarType::Type>(dtype), ctx.GetPlace(),
-          layout, library);
+          static_cast<framework::proto::VarType::Type>(dtype),
+          ctx.device_context());
     } else if (x_vars[0]->IsType<framework::SelectedRows>()) {
       for (auto& var : x_vars) {
         auto& value = var->Get<framework::SelectedRows>().value();
         if (value.IsInitialized()) {
           return framework::OpKernelType(framework::ToDataType(value.type()),
-                                         ctx.device_context(), layout, library);
+                                         ctx.device_context());
         }
       }
       // if input sparse vars are not initialized, use an default kernel type.
       return framework::OpKernelType(framework::proto::VarType::FP32,
-                                     ctx.device_context(), layout, library);
+                                     ctx.device_context());
     } else if (x_vars[0]->IsType<framework::LoDTensorArray>()) {
       for (auto& x_var : x_vars) {
         auto& array = x_var->Get<framework::LoDTensorArray>();
         for (auto& each : array) {
           if (each.numel() != 0) {
             return framework::OpKernelType(framework::ToDataType(each.type()),
-                                           ctx.device_context(), layout,
-                                           library);
+                                           ctx.device_context());
           }
         }
       }
@@ -133,9 +116,6 @@ class SumOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("X", "(vector<Tensor>) The input tensors of sum operator.")
         .AsDuplicable();
     AddOutput("Out", "(Tensor) The output tensor of sum operator.").Reuse("X");
-    AddAttr<bool>("use_mkldnn",
-                  "(bool, default false) Only used in mkldnn kernel")
-        .SetDefault(false);
     AddComment(R"DOC(
 Sum operator.
 
@@ -152,6 +132,7 @@ class SumOpVarTypeInference : public framework::VarTypeInference {
                   framework::BlockDesc* block) const override {
     auto& inputs = op_desc.Input("X");
     auto var_type = framework::proto::VarType::SELECTED_ROWS;
+
     for (auto& name : op_desc.Input("X")) {
       VLOG(10) << name << " "
                << block->FindRecursiveOrCreateVar(name).GetType();
@@ -225,7 +206,6 @@ namespace ops = paddle::operators;
 
 REGISTER_OPERATOR(sum, ops::SumOp, ops::SumOpMaker, ops::SumGradMaker,
                   ops::SumOpVarTypeInference);
-
 REGISTER_OP_CPU_KERNEL(
     sum, ops::SumKernel<paddle::platform::CPUDeviceContext, float>,
     ops::SumKernel<paddle::platform::CPUDeviceContext, double>,

paddle/fluid/operators/while_op.cc

@@ -203,11 +203,11 @@ class WhileGradOp : public framework::OperatorBase {
                 ->set_lod(inside_tensor.lod());
           }
         }
+
         auto new_inside_name = cur_scope.Rename(inside_grad_name);
         auto sum_op = framework::OpRegistry::CreateOp(
             "sum", {{"X", {pg_names[param_id], new_inside_name}}},
-            {{"Out", {pg_names[param_id]}}},
-            framework::AttributeMap{{"use_mkldnn", {false}}});
+            {{"Out", {pg_names[param_id]}}}, framework::AttributeMap{});
         sum_op->Run(cur_scope, dev_place);
         cur_scope.Rename(new_inside_name, inside_grad_name);
       }

paddle/fluid/platform/mkldnn_helper.h

@@ -99,11 +99,5 @@ inline mkldnn::memory::format GetMKLDNNFormat(const mkldnn::memory memory) {
       memory.get_primitive_desc().desc().data.format);
 }
 
-inline mkldnn::memory::format GetMKLDNNFormat(
-    const mkldnn::sum::primitive_desc& memory) {
-  return static_cast<mkldnn::memory::format>(
-      memory.dst_primitive_desc().desc().data.format);
-}
-
 }  // namespace platform
 }  // namespace paddle

python/paddle/fluid/backward.py

@@ -132,9 +132,9 @@ def _addup_repetitive_outputs_(op_descs):
     for idx, op_desc in enumerate(op_descs):
         for var_name in op_desc.input_arg_names():
             if len(renamed_vars[var_name]) > 1:
-                pending_sum_ops.append((_create_op_desc_(
-                    "sum", {"X": renamed_vars[var_name]}, {"Out": [var_name]},
-                    {"use_mkldnn": False}), idx))
+                pending_sum_ops.append(
+                    (_create_op_desc_("sum", {"X": renamed_vars[var_name]},
+                                      {"Out": [var_name]}, {}), idx))
                 renamed_vars[var_name] = [var_name]
         for var_name in op_desc.output_arg_names():
             if var_name == core.empty_var_name(
@@ -161,9 +161,8 @@ def _addup_repetitive_outputs_(op_descs):
                 renamed_vars[var_name].append(new_name)
     for var_name, inputs in renamed_vars.iteritems():
         if len(inputs) > 1:
-            pending_sum_ops.append(
-                (_create_op_desc_("sum", {"X": inputs}, {"Out": [var_name]},
-                                  {"use_mkldnn": False}), len(op_descs)))
+            pending_sum_ops.append((_create_op_desc_(
+                "sum", {"X": inputs}, {"Out": [var_name]}, {}), len(op_descs)))
     # sum_op descs are sorted according to their insert position
     for p in reversed(pending_sum_ops):
         op_descs.insert(p[1], p[0])