PaddlePaddle
diff --git a/‎paddle/fluid/framework/ir/CMakeLists.txt
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+7Lines changed: 7 additions & 0 deletions b/‎paddle/fluid/framework/ir/CMakeLists.txt
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diff --git a/‎paddle/fluid/framework/ir/graph_pattern_detector.cc
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+75Lines changed: 75 additions & 0 deletions b/‎paddle/fluid/framework/ir/graph_pattern_detector.cc
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diff --git a/‎paddle/fluid/framework/ir/graph_pattern_detector.h
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+27Lines changed: 27 additions & 0 deletions b/‎paddle/fluid/framework/ir/graph_pattern_detector.h
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diff --git a/‎paddle/fluid/framework/ir/mkldnn/self_attention_fuse_pass.cc
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+150Lines changed: 150 additions & 0 deletions b/‎paddle/fluid/framework/ir/mkldnn/self_attention_fuse_pass.cc
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diff --git a/‎paddle/fluid/framework/ir/mkldnn/self_attention_fuse_pass.h
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+41Lines changed: 41 additions & 0 deletions b/‎paddle/fluid/framework/ir/mkldnn/self_attention_fuse_pass.h
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diff --git a/‎paddle/fluid/inference/api/paddle_pass_builder.cc
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diff --git a/‎paddle/fluid/operators/fused/CMakeLists.txt
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+9Lines changed: 9 additions & 0 deletions b/‎paddle/fluid/operators/fused/CMakeLists.txt
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@@ -200,6 +200,13 @@ if(WITH_MKLDNN)
   pass_library(multi_gru_seq_fuse_pass inference DIR mkldnn)
   pass_library(quant_dequant_mkldnn_pass inference DIR mkldnn)
   pass_library(compute_propagate_scales_mkldnn_pass inference DIR mkldnn)
+  pass_library(self_attention_fuse_pass inference DIR mkldnn)
+  if(WITH_AVX
+     AND AVX512F_FOUND
+     AND AVX512F_FLAG)
+    set_target_properties(self_attention_fuse_pass
+                          PROPERTIES COMPILE_FLAGS "-mfma ${AVX512F_FLAG}")
+  endif()
 endif()
 
 if(WITH_IPU)
 
@@ -2615,6 +2615,81 @@ PDNode *patterns::VitAttention::operator()(PDNode *in) {
   return reshape2_out;
 }
 
+PDNode *patterns::SelfAttention::operator()(PDNode *in) {
+  in->AsInput();
+
+  std::unordered_set<std::string> matmul_ops{"matmul", "matmul_v2"};
+  auto transpose2_0_op =
+      pattern->NewNode(transpose2_0_op_repr())->assert_is_op("transpose2");
+  auto transpose2_0_out = pattern->NewNode(transpose2_0_out_repr())
+                              ->assert_is_op_output("transpose2", "Out")
+                              ->assert_is_op_input("slice", "Input")
+                              ->AsIntermediate();
+  auto slice_0_op = pattern->NewNode(slice_0_op_repr())->assert_is_op("slice");
+  auto slice_0_out = pattern->NewNode(slice_0_out_repr())
+                         ->assert_is_op_output("slice", "Out")
+                         ->assert_is_ops_input(matmul_ops, "X")
+                         ->AsIntermediate();
+  auto slice_1_op = pattern->NewNode(slice_1_op_repr())->assert_is_op("slice");
+  auto slice_1_out = pattern->NewNode(slice_1_out_repr())
+                         ->assert_is_op_output("slice", "Out")
+                         ->assert_is_op_input("transpose2", "X")
+                         ->AsIntermediate();
+  auto slice_2_op = pattern->NewNode(slice_2_op_repr())->assert_is_op("slice");
+  auto slice_2_out = pattern->NewNode(slice_2_out_repr())
+                         ->assert_is_op_output("slice", "Out")
+                         ->assert_is_ops_input(matmul_ops, "Y")
+                         ->AsIntermediate();
+  auto matmul_0_op =
+      pattern->NewNode(matmul_0_op_repr())->assert_is_ops(matmul_ops);
+  auto matmul_0_out = pattern->NewNode(matmul_0_out_repr())
+                          ->assert_is_ops_output(matmul_ops, "Out")
+                          ->assert_is_op_input("transpose2", "X")
+                          ->AsIntermediate();
+  auto matmul_1_op =
+      pattern->NewNode(matmul_1_op_repr())->assert_is_ops(matmul_ops);
+  auto matmul_1_out = pattern->NewNode(matmul_1_out_repr())
+                          ->assert_is_ops_output(matmul_ops, "Out")
+                          ->assert_is_op_input("softmax", "X")
+                          ->AsIntermediate();
+  auto transpose2_1_op =
+      pattern->NewNode(transpose2_1_op_repr())->assert_is_op("transpose2");
+  auto transpose2_1_out = pattern->NewNode(transpose2_1_out_repr())
+                              ->assert_is_op_output("transpose2", "Out")
+                              ->assert_is_ops_input(matmul_ops, "Y")
+                              ->AsIntermediate();
+  auto softmax_op =
+      pattern->NewNode(softmax_op_repr())->assert_is_op("softmax");
+  auto softmax_out = pattern->NewNode(softmax_out_repr())
+                         ->assert_is_op_output("softmax", "Out")
+                         ->assert_is_ops_input(matmul_ops, "X")
+                         ->AsIntermediate();
+  auto transpose2_2_op =
+      pattern->NewNode(transpose2_2_op_repr())->assert_is_op("transpose2");
+  auto transpose2_2_out = pattern->NewNode(transpose2_2_out_repr())
+                              ->assert_is_op_output("transpose2", "Out")
+                              ->AsOutput();
+  transpose2_0_op->LinksFrom({in});
+  transpose2_0_out->LinksFrom({transpose2_0_op});
+  slice_0_op->LinksFrom({transpose2_0_out});
+  slice_0_out->LinksFrom({slice_0_op});
+  slice_1_op->LinksFrom({transpose2_0_out});
+  slice_1_out->LinksFrom({slice_1_op});
+  slice_2_op->LinksFrom({transpose2_0_out});
+  slice_2_out->LinksFrom({slice_2_op});
+  transpose2_1_op->LinksFrom({slice_1_out});
+  transpose2_1_out->LinksFrom({transpose2_1_op});
+  matmul_1_op->LinksFrom({slice_0_out, transpose2_1_out});
+  matmul_1_out->LinksFrom({matmul_1_op});
+  softmax_op->LinksFrom({matmul_1_out});
+  softmax_out->LinksFrom({softmax_op});
+  matmul_0_op->LinksFrom({softmax_out, slice_2_out});
+  matmul_0_out->LinksFrom({matmul_0_op});
+  transpose2_2_op->LinksFrom({matmul_0_out});
+  transpose2_2_out->LinksFrom({transpose2_2_op});
+  return transpose2_2_out;
+}
+
 PDNode *patterns::ConvElementwiseadd2Act::operator()(
     PDNode *conv_in, const std::unordered_set<std::string> &conv_act_set) {
   auto conv_op = pattern->NewNode(conv_op_repr())->assert_is_op("conv2d");
 
@@ -1491,6 +1491,33 @@ struct VitAttention : public PatternBase {
   PATTERN_DECL_NODE(reshape2_out);
 };
 
+// self_attention in vit
+struct SelfAttention : public PatternBase {
+  SelfAttention(PDPattern* pattern, const std::string& name_scope)
+      : PatternBase(pattern, name_scope, "vit_block") {}
+
+  PDNode* operator()(PDNode* in);
+
+  PATTERN_DECL_NODE(transpose2_0_op);
+  PATTERN_DECL_NODE(transpose2_0_out);
+  PATTERN_DECL_NODE(transpose2_1_op);
+  PATTERN_DECL_NODE(transpose2_1_out);
+  PATTERN_DECL_NODE(transpose2_2_op);
+  PATTERN_DECL_NODE(transpose2_2_out);
+  PATTERN_DECL_NODE(matmul_0_op);
+  PATTERN_DECL_NODE(matmul_0_out);
+  PATTERN_DECL_NODE(matmul_1_op);
+  PATTERN_DECL_NODE(matmul_1_out);
+  PATTERN_DECL_NODE(slice_0_op);
+  PATTERN_DECL_NODE(slice_0_out);
+  PATTERN_DECL_NODE(slice_1_op);
+  PATTERN_DECL_NODE(slice_1_out);
+  PATTERN_DECL_NODE(slice_2_op);
+  PATTERN_DECL_NODE(slice_2_out);
+  PATTERN_DECL_NODE(softmax_op);
+  PATTERN_DECL_NODE(softmax_out);
+};
+
 // Conv + ElementwiseAdd + an activation
 // This pattern can further fuse the conv related ops after the conv+bn fusion.
 struct ConvElementwiseaddAct : public PatternBase {
 
@@ -0,0 +1,150 @@
+// 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
+//
+//     https://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.
+
+#include "paddle/fluid/framework/ir/mkldnn/self_attention_fuse_pass.h"
+
+#include <string>
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/op_version_registry.h"
+#include "paddle/fluid/string/pretty_log.h"
+#include "paddle/phi/kernels/funcs/blas/blas.h"
+
+#define GET_IR_NODE(node__) GET_IR_NODE_FROM_SUBGRAPH(node__, node__, pattern);
+#define GET_NODES                \
+  GET_IR_NODE(transpose2_0_op);  \
+  GET_IR_NODE(transpose2_0_out); \
+  GET_IR_NODE(slice_0_op);       \
+  GET_IR_NODE(slice_0_out);      \
+  GET_IR_NODE(slice_1_op);       \
+  GET_IR_NODE(slice_1_out);      \
+  GET_IR_NODE(slice_2_op);       \
+  GET_IR_NODE(slice_2_out);      \
+  GET_IR_NODE(matmul_0_op);      \
+  GET_IR_NODE(matmul_0_out);     \
+  GET_IR_NODE(matmul_1_op);      \
+  GET_IR_NODE(matmul_1_out);     \
+  GET_IR_NODE(transpose2_1_op);  \
+  GET_IR_NODE(transpose2_1_out); \
+  GET_IR_NODE(softmax_op);       \
+  GET_IR_NODE(softmax_out);      \
+  GET_IR_NODE(transpose2_2_op);  \
+  GET_IR_NODE(transpose2_2_out);
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+using string::PrettyLogDetail;
+
+void SelfAttentionFusePass::ApplyImpl(ir::Graph* graph) const {
+#if !defined(__AVX512F__) || !defined(PADDLE_WITH_MKLML) || \
+    !defined(PADDLE_WITH_MKLDNN)
+  LOG(WARNING) << "No-avx512 or MKL supported!";
+  return;
+#endif
+  // do something;
+  GraphPatternDetector gpd;
+  const std::string pattern_name = "self_attention_fuse";
+  FusePassBase::Init(pattern_name, graph);
+
+  // pattern
+  PDNode* x = gpd.mutable_pattern()
+                  ->NewNode("x")
+                  ->assert_is_op_input("transpose2", "X")
+                  ->AsInput();
+  patterns::SelfAttention pattern(gpd.mutable_pattern(), pattern_name);
+  pattern(x);
+
+  int fusion_count = 0;
+  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
+                     Graph* g) {
+    GET_NODES;
+    // do something;
+    OpDesc desc(transpose2_0_op->Op()->Block());
+    desc.SetType("self_dp_attention");
+    desc.SetInput("X", {subgraph.at(x)->Name()});
+    desc.SetOutput("Out", {transpose2_2_out->Name()});
+
+    std::vector<int64_t> in_shape = subgraph.at(x)->Var()->GetShape();
+    std::vector<int64_t> shape = transpose2_0_out->Var()->GetShape();
+    // in shape should be [batch_size, seq_len, 3, num_heads, head_size]
+    if (in_shape.size() != 5 || in_shape[2] != 3 || shape.size() != 5 ||
+        shape[0] != 3 || shape[2] != in_shape[3]) {
+      LOG(WARNING) << "Self-attention shape mismatch!";
+      return;
+    }
+    desc.SetAttr("head_number", static_cast<int>(shape[2]));
+    float alpha = 1.0;
+    if (matmul_1_op->Op()->HasAttr("alpha"))
+      alpha = PADDLE_GET_CONST(float, matmul_1_op->Op()->GetAttr("alpha"));
+    desc.SetAttr("alpha", alpha);
+
+    // Create a new node for the fused op.
+    auto self_attention_node = graph->CreateOpNode(&desc);
+
+    // Link inputs and outputs.
+    PADDLE_ENFORCE_NE(subgraph.count(x),
+                      0,
+                      platform::errors::NotFound(
+                          "Detector did not find input x of self attention."));
+
+    IR_NODE_LINK_TO(subgraph.at(x), self_attention_node);    // Input
+    IR_NODE_LINK_TO(self_attention_node, transpose2_2_out);  // Output
+
+    // Delete the unneeded nodes.
+    std::unordered_set<const Node*> marked_nodes({transpose2_0_op,
+                                                  transpose2_0_out,
+                                                  slice_0_op,
+                                                  slice_0_out,
+                                                  slice_1_op,
+                                                  slice_1_out,
+                                                  slice_2_op,
+                                                  slice_2_out,
+                                                  matmul_0_op,
+                                                  matmul_0_out,
+                                                  matmul_1_op,
+                                                  matmul_1_out,
+                                                  transpose2_1_op,
+                                                  transpose2_1_out,
+                                                  softmax_op,
+                                                  softmax_out,
+                                                  transpose2_2_op});
+
+    GraphSafeRemoveNodes(graph, marked_nodes);
+    ++fusion_count;
+  };
+  gpd(graph, handler);
+  AddStatis(fusion_count);
+  if (!Has("disable_logs") || !Get<bool>("disable_logs")) {
+    PrettyLogDetail(
+        "---    fused %d self attention (of scaled_dp_attention) with %s",
+        fusion_count,
+        pattern_name);
+  }
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(self_attention_fuse_pass,
+              paddle::framework::ir::SelfAttentionFusePass);
+REGISTER_PASS_CAPABILITY(self_attention_fuse_pass)
+    .AddCombination(
+        paddle::framework::compatible::OpVersionComparatorCombination()
+            .EQ("transpose2", 0)
+            .EQ("slice", 0)
+            .EQ("scale", 0)
+            .EQ("softmax", 0)
+            .EQ("matmul_v2", 0));
@@ -0,0 +1,41 @@
+// 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
+//
+//     https://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
+
+#include <string>
+
+#include "paddle/fluid/framework/ir/fuse_pass_base.h"
+#include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+// Fusing of self-attetion structure
+
+class Graph;
+
+class SelfAttentionFusePass : public FusePassBase {
+ public:
+  virtual ~SelfAttentionFusePass() {}
+
+ protected:
+  void ApplyImpl(ir::Graph* graph) const override;
+};
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
@@ -367,6 +367,7 @@ void CpuPassStrategy::EnableMKLDNN() {
              "fc_mkldnn_pass",
              "fc_act_mkldnn_fuse_pass",
              "fc_elementwise_add_mkldnn_fuse_pass",   //
+             "self_attention_fuse_pass",              //
              "batch_norm_act_fuse_pass",              //
              "softplus_activation_onednn_fuse_pass",  //
              "shuffle_channel_mkldnn_detect_pass",    //
 
@@ -11,6 +11,7 @@ register_operators(
   fusion_conv_inception_op
   fused_fc_elementwise_layernorm_op
   multihead_matmul_op
+  self_dp_attention_op
   skip_layernorm_op
   yolo_box_head_op
   yolo_box_post_op
@@ -33,6 +34,14 @@ register_operators(
 # fusion_gru_op does not have CUDA kernel
 op_library(fusion_gru_op)
 op_library(fusion_lstm_op)
+if(WITH_AVX
+   AND AVX512F_FOUND
+   AND AVX512F_FLAG
+   AND WITH_MKL)
+  op_library(self_dp_attention_op)
+  set_target_properties(self_dp_attention_op PROPERTIES COMPILE_FLAGS
+                                                        "-mfma ${AVX512F_FLAG}")
+endif()
 
 if(WITH_XPU)
   op_library(resnet_basic_block_op)