Final changes to introduce mem_desc to be hold in Tensor (#46768)

* first commit

- more fixes

- compilation fix

- compilation fix

- fix

- another fix

- yet another fix

- Fix

- fix to fused ops

- compilation fix

- compilation fix

- another compilation fix

- another fix

- fix

- fix

- fix

- fix

- yet another fix

- fix

- fix

- cosmetic fix

:- lint

- Revert some changes (to be brought back later)

- fix to build

- Added prototype of slice

- fix

compilation fix

- compilation fix

- fix

- fix

- Fix

- fix

 fix
	modified:   cmake/flags.cmake

* lint

* rerun of CI

* - Fix

* - lint

* - lint2

paddle/fluid/framework/tensor_util.cc

@@ -1245,11 +1245,6 @@ std::ostream& operator<<(std::ostream& os, const phi::DenseTensor& t) {
   os << "  - shape: [" << t.dims() << "]\n";
   os << "  - layout: " << phi::DataLayoutToString(t.layout()) << "\n";
 
-#ifdef PADDLE_WITH_MKLDNN
-  os << "  - format: "
-     << dnnl_fmt_tag2str(static_cast<dnnl_format_tag_t>(t.format())) << "\n";
-#endif
-
   DenseTensor tensor;
   tensor.Resize(t.dims());
   if (paddle::platform::is_cpu_place(t.place())) {

paddle/fluid/operators/fused/mkldnn/fusion_rnn_mkldnn.h

@@ -77,9 +77,12 @@ class RNNMKLDNNHandler : public platform::MKLDNNHandlerT<T, T_alg> {
     }
   }
 
-  bool is_NTC() {
-    return (platform::GetMKLDNNFormat(this->fwd_pd_->dst_desc()) ==
-            dnnl::memory::format_tag::ntc);
+  bool is_NTC() { return this->is_NTC(this->fwd_pd_->dst_desc()); }
+
+  bool is_NTC(const dnnl::memory::desc& md) {
+    auto ntc_md = dnnl::memory::desc(
+        md.dims(), md.data_type(), dnnl::memory::format_tag::ntc);
+    return md == ntc_md;
   }
 
   void reorderRNNdata(void* input_data,
@@ -165,8 +168,7 @@ class RNNMKLDNNHandler : public platform::MKLDNNHandlerT<T, T_alg> {
     auto* x_onednn_data = memory_p->get_data_handle();
     memset(x_onednn_data, 0, sizeof(T) * N * Ti * IC);
 
-    if (platform::GetMKLDNNFormat(this->fwd_pd_->src_desc()) ==
-        dnnl::memory::format_tag::ntc) {
+    if (is_NTC(this->fwd_pd_->src_desc())) {
       reorderRNNdata(x_data,
                      x_onednn_data,
                      input_lod,

paddle/fluid/operators/fused/mkldnn/multi_gru_mkldnn_op.cc

@@ -256,8 +256,7 @@ class MultiGRUHandler {
     auto* x_onednn_data = memory_p->get_data_handle();
     memset(x_onednn_data, 0, sizeof(T) * N_ * Ti_ * ICs[0]);
 
-    if (platform::GetMKLDNNFormat(gru_pds_[{0, L2R}]->src_desc()) ==
-        dnnl::memory::format_tag::ntc) {
+    if (isNTC(gru_pds_[{0, L2R}]->src_desc())) {
       reorderPPtoNTC(x_data, x_onednn_data, x_lod_, 0, L2R);
     } else {
       reorderPPtoTNC(x_data, x_onednn_data, x_lod_, 0, L2R);
@@ -601,16 +600,18 @@ class MultiGRUHandler {
   void reorderOutput(std::shared_ptr<dnnl::memory> mem, int layer) {
     auto* data = mem->get_data_handle();
     auto* hidden_data = to_void_cast(hidden_->mutable_data<Tout>(place_));
-    if (isNTC(layers_ - 1)) {
+
+    if (isNTC(gru_pds_[{layers_ - 1, L2R}]->dst_desc())) {
       reorderNTCtoPP(data, hidden_data, layers_ - 1);
     } else {
       reorderTNCtoPP(data, hidden_data, layers_ - 1);
     }
   }
 
-  bool isNTC(int layer) {
-    return (platform::GetMKLDNNFormat(gru_pds_[{layer, L2R}]->dst_desc()) ==
-            dnnl::memory::format_tag::ntc);
+  bool isNTC(const dnnl::memory::desc& md) {
+    auto ntc_md = dnnl::memory::desc(
+        md.dims(), md.data_type(), dnnl::memory::format_tag::ntc);
+    return md == ntc_md;
   }
 
   int getLayers() const { return layers_; }

paddle/fluid/operators/mkldnn/dequantize_mkldnn_op.cc

@@ -30,7 +30,6 @@ using platform::to_void_cast;
 using Tensor = phi::DenseTensor;
 using dnnl::stream;
 using phi::DataLayout;
-using platform::GetMKLDNNFormat;
 
 template <typename T>
 class DeQuantOpKernel : public framework::OpKernel<T> {

paddle/fluid/operators/mkldnn/fc_mkldnn_op.cc

@@ -29,7 +29,6 @@ using dnnl::stream;
 using framework::DDim;
 using framework::ExecutionContext;
 using LoDTensor = phi::DenseTensor;
-using platform::GetMKLDNNFormat;
 using platform::MKLDNNDeviceContext;
 using platform::MKLDNNGetDataType;
 using platform::to_void_cast;

paddle/fluid/operators/mkldnn/interpolate_mkldnn_op.cc

@@ -25,7 +25,6 @@ using dnnl::reorder;
 using dnnl::resampling_forward;
 using dnnl::stream;
 using phi::DataLayout;
-using platform::GetMKLDNNFormat;
 using platform::to_void_cast;
 
 template <typename T = float>

paddle/fluid/operators/mkldnn/matmul_v2_mkldnn_op.cc

@@ -16,7 +16,6 @@ limitations under the License. */
 namespace {
 using dnnl::memory;
 using paddle::framework::ExecutionContext;
-using paddle::platform::GetMKLDNNFormat;
 using paddle::platform::MatMulV2MKLDNNHandler;
 using paddle::platform::MKLDNNDeviceContext;
 using paddle::platform::MKLDNNFormatForSize;

paddle/fluid/operators/mkldnn/prelu_mkldnn_op.cc

@@ -20,7 +20,6 @@ namespace operators {
 
 using dnnl::memory;
 
-using platform::GetMKLDNNFormat;
 using platform::MKLDNNDeviceContext;
 using platform::MKLDNNGetDataType;
 using platform::to_void_cast;

paddle/fluid/operators/mkldnn/quantize_mkldnn_op.cc

@@ -29,7 +29,6 @@ using platform::to_void_cast;
 using Tensor = phi::DenseTensor;
 using dnnl::stream;
 using phi::DataLayout;
-using platform::GetMKLDNNFormat;
 
 template <typename T>
 class QuantOpKernel : public framework::OpKernel<T> {

paddle/fluid/operators/mkldnn/reshape_mkldnn_op.cc

@@ -30,7 +30,6 @@ enum class ReshapeKernelOpName {
 namespace paddle {
 namespace operators {
 
-using platform::GetMKLDNNFormat;
 using platform::to_void_cast;
 
 static std::vector<int> extract_shape(
@@ -83,13 +82,13 @@ class ReshapeMKLDNNKernel : public framework::OpKernel<T> {
         onednn_engine);
 
     auto reorder_src_memory_p = reorder_handler.AcquireSrcMemory(
-        x->format(), platform::to_void_cast(x->data<T>()));
+        x->mem_desc(), platform::to_void_cast(x->data<T>()));
     out->Resize(x_dims);  // to match x numel, format is changed later
     // reorder is done into a plain tag to allow usage with blocked formats
     auto reorder_dst_memory_p = reorder_handler.AcquireDstMemory(
         out, getPlainFormatTag(x), ctx.GetPlace());
-    auto reorder_p = reorder_handler.AcquireReorder(reorder_src_memory_p,
-                                                    reorder_dst_memory_p);
+    auto reorder_p = reorder_handler.AcquireReorder(reorder_dst_memory_p,
+                                                    reorder_src_memory_p);
 
     auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
     reorder_p->execute(astream, *reorder_src_memory_p, *reorder_dst_memory_p);
@@ -97,9 +96,8 @@ class ReshapeMKLDNNKernel : public framework::OpKernel<T> {
     astream.wait();
 
     out->Resize(out_dims);
-    out->set_layout(phi::DataLayout::kMKLDNN);
-    out->set_format(GetMKLDNNFormat(
-        reorder_dst_memory_p->get_desc().reshape(phi::vectorize(out_dims))));
+    out->set_mem_desc(
+        reorder_dst_memory_p->get_desc().reshape(phi::vectorize(out_dims)));
   }
 
   void InferInOutShape(const framework::ExecutionContext& ctx,
@@ -358,20 +356,18 @@ class ReshapeGradMKLDNNKernel : public ReshapeMKLDNNKernel<T, op_name> {
         onednn_engine);
 
     auto reorder_src_memory_p = reorder_handler.AcquireSrcMemory(
-        dout->format(), platform::to_void_cast(dout->data<T>()));
+        dout->mem_desc(), platform::to_void_cast(dout->data<T>()));
     auto reorder_dst_memory_p = reorder_handler.AcquireDstMemory(
         dx, this->getPlainFormatTag(dout), ctx.GetPlace());
-    auto reorder_p = reorder_handler.AcquireReorder(reorder_src_memory_p,
-                                                    reorder_dst_memory_p);
+    auto reorder_p = reorder_handler.AcquireReorder(reorder_dst_memory_p,
+                                                    reorder_src_memory_p);
 
     auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
     reorder_p->execute(astream, *reorder_src_memory_p, *reorder_dst_memory_p);
     astream.wait();
 
     dx->Resize(dx_dims);
-    dx->set_layout(phi::DataLayout::kMKLDNN);
-    dx->set_format(GetMKLDNNFormat(
-        reorder_dst_memory_p->get_desc().reshape(phi::vectorize(dx_dims))));
+    reorder_dst_memory_p->get_desc().reshape(phi::vectorize(dx_dims));
   }
 
   void InferOutputShapeInGrad(const framework::ExecutionContext& ctx,

paddle/fluid/platform/mkldnn_helper.h

@@ -201,164 +201,6 @@ inline void Reorder(dnnl::memory src,
   astream.wait();
 }
 
-inline dnnl::memory::format_tag GetMKLDNNFormat(dnnl::memory::desc mem_desc) {
-  auto ndims = mem_desc.data.ndims;
-  auto strides = mem_desc.data.format_desc.blocking.strides;
-  auto inner_nblks = mem_desc.data.format_desc.blocking.inner_nblks;
-  auto inner_blks = mem_desc.data.format_desc.blocking.inner_blks;
-  auto inner_idxs = mem_desc.data.format_desc.blocking.inner_idxs;
-
-  if (ndims == 1) {
-    return dnnl::memory::format_tag::x;
-  } else if (ndims == 2) {
-    if (inner_nblks == 0) {
-      if (strides[0] >= strides[1]) {
-        return dnnl::memory::format_tag::nc;
-      } else {
-        return dnnl::memory::format_tag::cn;
-      }
-    }
-  } else if (ndims == 3) {
-    if (inner_nblks == 0) {
-      if (strides[0] >= strides[1] && strides[1] >= strides[2]) {
-        return dnnl::memory::format_tag::ncw;
-      } else if (strides[1] >= strides[0] && strides[0] >= strides[2]) {
-        return dnnl::memory::format_tag::ntc;
-      } else {
-        return dnnl::memory::format_tag::nwc;
-      }
-    }
-  } else if (ndims == 4) {
-    if (inner_nblks == 0) {
-      if (strides[0] >= strides[1] && strides[1] >= strides[2] &&
-          strides[2] >= strides[3]) {
-        return dnnl::memory::format_tag::abcd;
-      } else if (strides[2] >= strides[3] && strides[3] >= strides[1] &&
-                 strides[1] >= strides[0]) {
-        return dnnl::memory::format_tag::cdba;
-      } else if (strides[0] >= strides[2] && strides[2] >= strides[3] &&
-                 strides[3] >= strides[1]) {
-        return dnnl::memory::format_tag::acdb;
-      } else if (strides[0] >= strides[1] && strides[1] >= strides[3] &&
-                 strides[3] >= strides[2]) {
-        return dnnl::memory::format_tag::abdc;
-      } else if (strides[2] >= strides[3] && strides[3] >= strides[1] &&
-                 strides[1] >= strides[0]) {
-        return dnnl::memory::format_tag::cdba;
-      } else {
-        return dnnl::memory::format_tag::dcab;
-      }
-    } else if (inner_nblks == 1) {
-      if (inner_blks[0] == 16 && inner_idxs[0] == 1) {
-        return dnnl::memory::format_tag::nChw16c;
-      } else if (inner_blks[0] == 8 && inner_idxs[0] == 1) {
-        return dnnl::memory::format_tag::nChw8c;
-      } else if (inner_blks[0] == 8 && inner_idxs[0] == 0) {
-        if (strides[0] >= strides[2] && strides[2] >= strides[3] &&
-            strides[3] >= strides[1]) {
-          return dnnl::memory::format_tag::Acdb8a;
-        }
-      } else if (inner_blks[0] == 4 && inner_idxs[0] == 1) {
-        return dnnl::memory::format_tag::nChw4c;
-      } else if (inner_blks[0] == 16 && inner_idxs[0] == 0) {
-        if (strides[0] >= strides[2] && strides[2] >= strides[3] &&
-            strides[3] >= strides[1]) {
-          return dnnl::memory::format_tag::Acdb16a;
-        }
-      }
-    } else if (inner_nblks == 2) {
-      if (inner_blks[0] == 16 && inner_blks[1] == 16) {
-        if (inner_idxs[0] == 1 && inner_idxs[1] == 0) {
-          return dnnl::memory::format_tag::OIhw16i16o;
-        }
-      } else if (inner_blks[0] == 8 && inner_blks[1] == 8) {
-        if (inner_idxs[0] == 1 && inner_idxs[1] == 0) {
-          return dnnl::memory::format_tag::OIhw8i8o;
-        }
-      }
-    }
-  } else if (ndims == 5) {
-    if (inner_nblks == 0) {
-      if (strides[0] >= strides[1] && strides[1] >= strides[2] &&
-          strides[2] >= strides[3] && strides[3] >= strides[4]) {
-        return dnnl::memory::format_tag::abcde;
-      } else if (strides[0] >= strides[2] && strides[2] >= strides[1] &&
-                 strides[1] >= strides[3] && strides[3] >= strides[4]) {
-        return dnnl::memory::format_tag::acbde;
-      } else if (strides[0] >= strides[2] && strides[2] >= strides[3] &&
-                 strides[3] >= strides[4] && strides[4] >= strides[1]) {
-        return dnnl::memory::format_tag::acdeb;
-      }
-    } else if (inner_nblks == 1) {
-      if (inner_blks[0] == 4 && inner_idxs[0] == 1) {
-        if (strides[0] >= strides[1] && strides[1] >= strides[2] &&
-            strides[2] >= strides[3] && strides[3] >= strides[4]) {
-          return dnnl::memory::format_tag::aBcde4b;
-        }
-      } else if (inner_blks[0] == 8 && inner_idxs[0] == 0) {
-        if (strides[0] >= strides[2] && strides[2] >= strides[3] &&
-            strides[3] >= strides[4] && strides[4] >= strides[1]) {
-          return dnnl::memory::format_tag::Acdeb8a;
-        }
-        if (strides[0] >= strides[1] && strides[1] >= strides[2] &&
-            strides[2] >= strides[3] && strides[3] >= strides[4]) {
-          return dnnl::memory::format_tag::Abcde8a;
-        }
-      } else if (inner_blks[0] == 8 && inner_idxs[0] == 1) {
-        if (strides[0] >= strides[1] && strides[1] >= strides[2] &&
-            strides[2] >= strides[3] && strides[3] >= strides[4]) {
-          return dnnl::memory::format_tag::aBcde8b;
-        }
-      } else if (inner_blks[0] == 16 && inner_idxs[0] == 0) {
-        if (strides[0] >= strides[2] && strides[2] >= strides[3] &&
-            strides[3] >= strides[4] && strides[4] >= strides[1]) {
-          return dnnl::memory::format_tag::Acdeb16a;
-        }
-        if (strides[0] >= strides[1] && strides[1] >= strides[2] &&
-            strides[2] >= strides[3] && strides[3] >= strides[4]) {
-          return dnnl::memory::format_tag::Abcde16a;
-        }
-      } else if (inner_blks[0] == 16 && inner_idxs[0] == 1) {
-        if (strides[0] >= strides[1] && strides[1] >= strides[2] &&
-            strides[2] >= strides[3] && strides[3] >= strides[4]) {
-          return dnnl::memory::format_tag::aBcde16b;
-        }
-      }
-    }
-  } else if (ndims == 6) {
-    if (inner_nblks == 0) {
-      if (strides[0] >= strides[1] && strides[1] >= strides[2] &&
-          strides[2] >= strides[3] && strides[3] >= strides[4] &&
-          strides[4] >= strides[5]) {
-        return dnnl::memory::format_tag::abcdef;
-      } else if (strides[0] >= strides[2] && strides[2] >= strides[1] &&
-                 strides[1] >= strides[3] && strides[3] >= strides[4] &&
-                 strides[4] >= strides[5]) {
-        return dnnl::memory::format_tag::acbdef;
-      }
-    }
-  }
-  // DEBUG CODE - KEEP UNTILL TENSOR.MEMORY_DESC IMPLEMENTED
-  // std::cout<<"@@@@@@@@@@ UNDEFINED FORMAT @@@@@@@@@@@@@@@@@@@"<<std::endl;
-  // std::cout<<"NDIMS: "<<ndims<<std::endl;
-  // std::cout<<"INNER_NBLKS: "<<inner_nblks<<std::endl;
-  // for (int i=0;i<ndims;++i) {
-  //   std::cout<<"STRIDE["<<i<<"]: "<<strides[i]<<std::endl;
-  // }
-  // for (int i=0;i<inner_nblks;++i) {
-  //   std::cout<<"INNER_BLKS["<<i<<"]: "<<inner_blks[i]<<std::endl;
-  // }
-  // for (int i=0;i<inner_nblks;++i) {
-  //   std::cout<<"INNER_IDXS["<<i<<"]: "<<inner_idxs[i]<<std::endl;
-  // }
-  return dnnl::memory::format_tag::undef;
-}
-
-inline dnnl::memory::format_tag GetMKLDNNFormat(const dnnl::memory memory) {
-  auto mem_desc = memory.get_desc();
-  return GetMKLDNNFormat(mem_desc);
-}
-
 inline dnnl::memory::format_tag GetPlainMKLDNNFormat(int tensor_rank) {
   switch (tensor_rank) {
     case 1:

paddle/fluid/platform/mkldnn_utils.h

-/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-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. */
-
-#pragma once
-
-// NOTE:
-// GetMKLDNNFormat function is here temporarily. It is
-// needed because without them forward declaration was causing an error when
-// building with "-DWITH_TESTING=ON". This file will be deleted after completing
-// md-related refactoring
-
-namespace paddle {
-namespace platform {
-
-inline dnnl::memory::format_tag GetMKLDNNFormat(dnnl::memory::desc mem_desc) {
-  auto ndims = mem_desc.data.ndims;
-  auto strides = mem_desc.data.format_desc.blocking.strides;
-  auto inner_nblks = mem_desc.data.format_desc.blocking.inner_nblks;
-  auto inner_blks = mem_desc.data.format_desc.blocking.inner_blks;
-  auto inner_idxs = mem_desc.data.format_desc.blocking.inner_idxs;
-
-  if (ndims == 1) {
-    return dnnl::memory::format_tag::x;
-  } else if (ndims == 2) {
-    if (inner_nblks == 0) {
-      if (strides[0] >= strides[1]) {
-        return dnnl::memory::format_tag::nc;
-      } else {
-        return dnnl::memory::format_tag::cn;
-      }
-    }
-  } else if (ndims == 3) {
-    if (inner_nblks == 0) {
-      if (strides[0] >= strides[1] && strides[1] >= strides[2]) {
-        return dnnl::memory::format_tag::ncw;
-      } else if (strides[1] >= strides[0] && strides[0] >= strides[2]) {
-        return dnnl::memory::format_tag::ntc;
-      } else {
-        return dnnl::memory::format_tag::nwc;
-      }
-    }
-  } else if (ndims == 4) {
-    if (inner_nblks == 0) {
-      if (strides[0] >= strides[1] && strides[1] >= strides[2] &&
-          strides[2] >= strides[3]) {
-        return dnnl::memory::format_tag::abcd;
-      } else if (strides[2] >= strides[3] && strides[3] >= strides[1] &&
-                 strides[1] >= strides[0]) {
-        return dnnl::memory::format_tag::cdba;
-      } else if (strides[0] >= strides[2] && strides[2] >= strides[3] &&
-                 strides[3] >= strides[1]) {
-        return dnnl::memory::format_tag::acdb;
-      } else if (strides[0] >= strides[1] && strides[1] >= strides[3] &&
-                 strides[3] >= strides[2]) {
-        return dnnl::memory::format_tag::abdc;
-      } else if (strides[2] >= strides[3] && strides[3] >= strides[1] &&
-                 strides[1] >= strides[0]) {
-        return dnnl::memory::format_tag::cdba;
-      } else {
-        return dnnl::memory::format_tag::dcab;
-      }
-    } else if (inner_nblks == 1) {
-      if (inner_blks[0] == 16 && inner_idxs[0] == 1) {
-        return dnnl::memory::format_tag::nChw16c;
-      } else if (inner_blks[0] == 8 && inner_idxs[0] == 1) {
-        return dnnl::memory::format_tag::nChw8c;
-      } else if (inner_blks[0] == 8 && inner_idxs[0] == 0) {
-        if (strides[0] >= strides[2] && strides[2] >= strides[3] &&
-            strides[3] >= strides[1]) {
-          return dnnl::memory::format_tag::Acdb8a;
-        }
-      } else if (inner_blks[0] == 4 && inner_idxs[0] == 1) {
-        return dnnl::memory::format_tag::nChw4c;
-      } else if (inner_blks[0] == 16 && inner_idxs[0] == 0) {
-        if (strides[0] >= strides[2] && strides[2] >= strides[3] &&
-            strides[3] >= strides[1]) {
-          return dnnl::memory::format_tag::Acdb16a;
-        }
-      }
-    } else if (inner_nblks == 2) {
-      if (inner_blks[0] == 16 && inner_blks[1] == 16) {
-        if (inner_idxs[0] == 1 && inner_idxs[1] == 0) {
-          return dnnl::memory::format_tag::OIhw16i16o;
-        }
-      } else if (inner_blks[0] == 8 && inner_blks[1] == 8) {
-        if (inner_idxs[0] == 1 && inner_idxs[1] == 0) {
-          return dnnl::memory::format_tag::OIhw8i8o;
-        }
-      }
-    }
-  } else if (ndims == 5) {
-    if (inner_nblks == 0) {
-      if (strides[0] >= strides[1] && strides[1] >= strides[2] &&
-          strides[2] >= strides[3] && strides[3] >= strides[4]) {
-        return dnnl::memory::format_tag::abcde;
-      } else if (strides[0] >= strides[2] && strides[2] >= strides[1] &&
-                 strides[1] >= strides[3] && strides[3] >= strides[4]) {
-        return dnnl::memory::format_tag::acbde;
-      } else if (strides[0] >= strides[2] && strides[2] >= strides[3] &&
-                 strides[3] >= strides[4] && strides[4] >= strides[1]) {
-        return dnnl::memory::format_tag::acdeb;
-      }
-    } else if (inner_nblks == 1) {
-      if (inner_blks[0] == 8 && inner_idxs[0] == 0) {
-        if (strides[0] >= strides[2] && strides[2] >= strides[3] &&
-            strides[3] >= strides[4] && strides[4] >= strides[1]) {
-          return dnnl::memory::format_tag::Acdeb8a;
-        }
-        if (strides[0] >= strides[1] && strides[1] >= strides[2] &&
-            strides[2] >= strides[3] && strides[3] >= strides[4]) {
-          return dnnl::memory::format_tag::Abcde8a;
-        }
-      } else if (inner_blks[0] == 8 && inner_idxs[0] == 1) {
-        if (strides[0] >= strides[1] && strides[1] >= strides[2] &&
-            strides[2] >= strides[3] && strides[3] >= strides[4]) {
-          return dnnl::memory::format_tag::aBcde8b;
-        }
-      } else if (inner_blks[0] == 16 && inner_idxs[0] == 0) {
-        if (strides[0] >= strides[2] && strides[2] >= strides[3] &&
-            strides[3] >= strides[4] && strides[4] >= strides[1]) {
-          return dnnl::memory::format_tag::Acdeb16a;
-        }
-        if (strides[0] >= strides[1] && strides[1] >= strides[2] &&
-            strides[2] >= strides[3] && strides[3] >= strides[4]) {
-          return dnnl::memory::format_tag::Abcde16a;
-        }
-      } else if (inner_blks[0] == 16 && inner_idxs[0] == 1) {
-        if (strides[0] >= strides[1] && strides[1] >= strides[2] &&
-            strides[2] >= strides[3] && strides[3] >= strides[4]) {
-          return dnnl::memory::format_tag::aBcde16b;
-        }
-      }
-    }
-  } else if (ndims == 6) {
-    if (inner_nblks == 0) {
-      if (strides[0] >= strides[1] && strides[1] >= strides[2] &&
-          strides[2] >= strides[3] && strides[3] >= strides[4] &&
-          strides[4] >= strides[5]) {
-        return dnnl::memory::format_tag::abcdef;
-      } else if (strides[0] >= strides[2] && strides[2] >= strides[1] &&
-                 strides[1] >= strides[3] && strides[3] >= strides[4] &&
-                 strides[4] >= strides[5]) {
-        return dnnl::memory::format_tag::acbdef;
-      }
-    }
-  }
-  // DEBUG CODE - KEEP UNTILL TENSOR.MEMORY_DESC IMPLEMENTED
-  // std::cout<<"@@@@@@@@@@ UNDEFINED FORMAT @@@@@@@@@@@@@@@@@@@"<<std::endl;
-  // std::cout<<"NDIMS: "<<ndims<<std::endl;
-  // std::cout<<"INNER_NBLKS: "<<inner_nblks<<std::endl;
-  // for (int i=0;i<ndims;++i) {
-  //   std::cout<<"STRIDE["<<i<<"]: "<<strides[i]<<std::endl;
-  // }
-  // for (int i=0;i<inner_nblks;++i) {
-  //   std::cout<<"INNER_BLKS["<<i<<"]: "<<inner_blks[i]<<std::endl;
-  // }
-  // for (int i=0;i<inner_nblks;++i) {
-  //   std::cout<<"INNER_IDXS["<<i<<"]: "<<inner_idxs[i]<<std::endl;
-  // }
-  return dnnl::memory::format_tag::undef;
-}
-
-}  // namespace platform
-}  // namespace paddle

paddle/phi/backends/onednn/onednn_reuse.h

@@ -860,6 +860,16 @@ class ReorderOneDNNHandler {
     }
   }
 
+  std::shared_ptr<dnnl::memory> AcquireDstMemory(
+      DenseTensor* output,
+      const std::vector<int64_t>& dims,
+      const std::vector<int64_t>& strides,
+      Place place) {
+    auto dst_md = dnnl::memory::desc(dims, dtype_dst_, strides);
+    auto dst_data = output->mutable_data(place, ptype_dst_, dst_md.get_size());
+    return std::make_shared<dnnl::memory>(dst_md, engine_, dst_data);
+  }
+
   std::shared_ptr<dnnl::memory> AcquireDstMemory(
       DenseTensor* output,
       const std::vector<int64_t>& dims,

paddle/phi/core/dense_tensor.cc

@@ -58,7 +58,6 @@ DenseTensor::DenseTensor(const DenseTensor& other) : meta_(other.meta()) {
   inplace_version_counter_ = other.inplace_version_counter_;
 
 #ifdef PADDLE_WITH_MKLDNN
-  format_ = other.format_;
   mem_desc_ = other.mem_desc_;
 #endif
 }
@@ -70,7 +69,6 @@ DenseTensor& DenseTensor::operator=(const DenseTensor& other) {
       std::move(CopyStorageProperties(other.storage_properties_));
   inplace_version_counter_ = other.inplace_version_counter_;
 #ifdef PADDLE_WITH_MKLDNN
-  format_ = other.format_;
   mem_desc_ = other.mem_desc_;
 #endif
   return *this;
@@ -82,7 +80,6 @@ DenseTensor& DenseTensor::operator=(DenseTensor&& other) {
   storage_properties_ = std::move(other.storage_properties_);
   std::swap(inplace_version_counter_, other.inplace_version_counter_);
 #ifdef PADDLE_WITH_MKLDNN
-  format_ = other.format_;
   mem_desc_ = other.mem_desc_;
 #endif
   return *this;

paddle/phi/core/dense_tensor.h

@@ -274,16 +274,6 @@ In the final state, we should come up with a MKLDNN_Tensor and move the
 following codes there.
 */
 #ifdef PADDLE_WITH_MKLDNN
-  /**
-   * @brief the detail format of memory block which have layout as kMKLDNN
-   *
-   * @note MKLDNN lib support various memory format like nchw, nhwc, nChw8C,
-   *       nChw16c, etc. For a MKLDNN memory block, layout will be set as
-   *       DataLayout::kMKLDNN meanwhile detail memory format will be kept in
-   *       this field.
-   */
-  dnnl::memory::format_tag format_ = dnnl::memory::format_tag::undef;
-
   /// \brief memory descriptor of tensor which have layout set as kMKLDNN
   dnnl::memory::desc mem_desc_;
 #endif

paddle/phi/core/dense_tensor.inl

@@ -123,12 +123,6 @@ inline void set_mem_desc(const dnnl::memory::desc& mem_desc) {
   meta_.layout = DataLayout::kMKLDNN;
 }
 
-dnnl::memory::format_tag format() const;
-
-inline void set_format(const dnnl::memory::format_tag format) {
-  format_ = format;
-}
-
 #endif
 
 /* ------------------------------ */

paddle/phi/core/dense_tensor_impl.cc

@@ -19,10 +19,6 @@ limitations under the License. */
 #include "paddle/phi/core/compat/convert_utils.h"
 #include "paddle/phi/core/dense_tensor.h"
 
-#ifdef PADDLE_WITH_MKLDNN
-#include "paddle/fluid/platform/mkldnn_utils.h"
-#endif
-
 namespace phi {
 /* --------------------------- */
 /*   From phi::DenseTensor    */
@@ -348,16 +344,7 @@ std::vector<DenseTensor> DenseTensor::Chunk(int64_t chunks,
 }
 
 #ifdef PADDLE_WITH_MKLDNN
-dnnl::memory::desc DenseTensor::mem_desc() const {
-  return mem_desc_ ? mem_desc_
-                   : dnnl::memory::desc(phi::vectorize(meta_.dims),
-                                        phi::TransToOneDNNDataType(meta_.dtype),
-                                        format_);
-}
-
-dnnl::memory::format_tag DenseTensor::format() const {
-  return mem_desc_ ? paddle::platform::GetMKLDNNFormat(mem_desc_) : format_;
-}
+dnnl::memory::desc DenseTensor::mem_desc() const { return mem_desc_; }
 #endif
 
 // NOTE: For historical reasons, this interface has a special behavior,
@@ -373,7 +360,6 @@ DenseTensor& DenseTensor::ShareDataWith(const DenseTensor& src) {
   storage_properties_ =
       std::move(CopyStorageProperties(src.storage_properties_));
 #ifdef PADDLE_WITH_MKLDNN
-  format_ = src.format_;
   mem_desc_ = src.mem_desc_;
 #endif
   return *this;

paddle/phi/kernels/onednn/split_kernel.cc

@@ -19,6 +19,23 @@
 
 namespace phi {
 
+const std::vector<int64_t> get_slice_strides(
+    const std::vector<int64_t>& out_vec_dims,
+    const dnnl::memory::desc& full_md,
+    int axis) {
+  auto strides = full_md.data.format_desc.blocking.strides;
+  auto ndims = full_md.data.ndims;
+  auto full_dims = full_md.data.dims;
+  auto splitted_stride = strides[axis];
+  std::vector<int64_t> slice_strides(ndims, splitted_stride);
+  for (int16_t i = 0; i < ndims; ++i) {
+    slice_strides[i] = strides[i] > splitted_stride
+                           ? (strides[i] / full_dims[axis]) * out_vec_dims[axis]
+                           : strides[i];
+  }
+  return slice_strides;
+}
+
 template <typename T, typename Context>
 void SplitKernel(const Context& dev_ctx,
                  const DenseTensor& x,
@@ -49,7 +66,10 @@ void SplitKernel(const Context& dev_ctx,
         out_vec_dims, offset, reorder_src_memory_p);
 
     auto reorder_dst_memory_p = reorder_handler.AcquireDstMemory(
-        out[i], out_vec_dims, x.format(), dev_ctx.GetPlace());
+        out[i],
+        out_vec_dims,
+        get_slice_strides(out_vec_dims, x.mem_desc(), axis),
+        dev_ctx.GetPlace());
     auto reorder_p =
         reorder_handler.AcquireReorder(reorder_dst_memory_p, slice_mem_p);