added fusing matmul-transpose-reshape pass (#23866)

paddle/fluid/framework/ddim.cc

@@ -131,5 +131,67 @@ DDim stride_numel(const DDim& ddim) {
   return strides;
 }
 
+DDim DDim::reshape(const std::vector<int>& shape) const {
+  const int64_t copy_dim_val = 0;
+  const DDim& in_dims = *this;
+  DDim out_dims;
+  out_dims.rank_ = shape.size();
+  for (size_t i = 0; i < shape.size(); ++i) {
+    if (shape[i] == copy_dim_val) {
+      PADDLE_ENFORCE_LT(static_cast<int>(i), in_dims.size(),
+                        platform::errors::InvalidArgument(
+                            "Index %d of shape under which the value of 0 "
+                            "is stored, must be lower than the number of "
+                            "old dimensions. But received shape[%d] = 0, "
+                            "dimensions = %d, shape = [%s].",
+                            i, in_dims.size(), in_dims));
+      out_dims[i] = in_dims[i];
+    } else {
+      out_dims[i] = shape[i];
+    }
+  }
+  return out_dims;
+}
+
+DDim DDim::transpose(const std::vector<int>& axis) const {
+  const DDim& in_dims = *this;
+  size_t in_rank = in_dims.size();
+  size_t axis_size = axis.size();
+
+  PADDLE_ENFORCE_EQ(
+      in_rank, axis_size,
+      platform::errors::InvalidArgument("The input dimension's size "
+                                        "should be equal to the axis's size. "
+                                        "But received dimension is %d, "
+                                        "axis's size is %d",
+                                        in_rank, axis_size));
+
+  std::vector<int> count(axis_size, 0);
+  for (size_t i = 0; i < axis_size; i++) {
+    PADDLE_ENFORCE_LT(axis[i], static_cast<int>(axis_size),
+                      platform::errors::InvalidArgument(
+                          "ValueError: Each element of axis must appear "
+                          "exactly once in the range from 0 to (dims - 1), "
+                          "where the dims is the axis's size, "
+                          "but received axis[%d] is %d, axis_size is %d",
+                          i, axis[i], axis_size));
+    PADDLE_ENFORCE_EQ(
+        ++count[axis[i]], 1,
+        platform::errors::InvalidArgument(
+            "ValueError: Each element of axis should "
+            "be a unique value range from 0 to (dims - 1), "
+            "where the dims is the axis's size, "
+            "unique value means this axis value can appear only once. "
+            "But received count[axis[%d]] is %d",
+            i, count[axis[i]]));
+  }
+
+  DDim out_dims(in_dims);
+  for (size_t i = 0; i < axis_size; i++) {
+    out_dims[i] = in_dims[axis[i]];
+  }
+  return out_dims;
+}
+
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/ddim.h

@@ -126,6 +126,10 @@ class DDim {
 
   std::string to_str() const;
 
+  DDim reshape(const std::vector<int>& shape) const;
+
+  DDim transpose(const std::vector<int>& axis) const;
+
  private:
   template <int D>
   inline Dim<D>& UnsafeCast() {

paddle/fluid/framework/ir/CMakeLists.txt

@@ -97,6 +97,7 @@ if(WITH_MKLDNN)
     pass_library(cpu_quantize_placement_pass base DIR mkldnn)
     pass_library(cpu_quantize_pass inference DIR mkldnn)
     pass_library(cpu_quantize_squash_pass inference DIR mkldnn)
+    pass_library(matmul_transpose_reshape_fuse_pass inference DIR mkldnn)
 endif()
 
 cc_library(fuse_bn_act_pass SRCS fuse_bn_act_pass.cc DEPS pass graph_pattern_detector )
@@ -144,4 +145,5 @@ if (WITH_MKLDNN)
     cc_test(test_cpu_quantize_placement_pass SRCS mkldnn/cpu_quantize_placement_pass_tester.cc DEPS cpu_quantize_placement_pass)
     cc_test(test_cpu_quantize_pass SRCS mkldnn/cpu_quantize_pass_tester.cc DEPS cpu_quantize_pass naive_executor)
     cc_test(test_cpu_quantize_squash_pass SRCS mkldnn/cpu_quantize_squash_pass_tester.cc DEPS cpu_quantize_squash_pass naive_executor)
+    cc_test(test_matmul_transpose_reshape_fuse_pass SRCS mkldnn/matmul_transpose_reshape_fuse_pass_tester.cc DEPS matmul_transpose_reshape_fuse_pass)
 endif ()

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -2147,6 +2147,43 @@ void patterns::DeleteQuantDequantOpPattern::operator()() {
   any_op2->LinksFrom({quant_dequant_out});
 }
 
+PDNode *patterns::MatmulTransposeReshapePattern::operator()() {
+  auto reshape_op =
+      pattern->NewNode(reshape_op_repr())->assert_is_op("reshape2");
+  auto transpose_op =
+      pattern->NewNode(transpose_op_repr())->assert_is_op("transpose2");
+  auto matmul_op = pattern->NewNode(matmul_op_repr())->assert_is_op("matmul");
+
+  auto matmul_out = pattern->NewNode(matmul_out_repr())
+                        ->AsInput()
+                        ->assert_is_op_output("matmul", "Out")
+                        ->assert_is_op_input("transpose2", "X");
+
+  auto transpose_out = pattern->NewNode(transpose_out_repr())
+                           ->AsIntermediate()
+                           ->assert_is_op_output("transpose2", "Out")
+                           ->assert_is_op_input("reshape2", "X");
+
+  auto transpose_out_xshape = pattern->NewNode(transpose_out_xshape_repr())
+                                  ->AsIntermediate()
+                                  ->assert_is_op_output("transpose2", "XShape");
+
+  auto reshape_out = pattern->NewNode(reshape_out_repr())
+                         ->AsOutput()
+                         ->assert_is_op_output("reshape2");
+
+  auto reshape_out_xshape = pattern->NewNode(reshape_out_xshape_repr())
+                                ->AsIntermediate()
+                                ->assert_is_op_output("reshape2", "XShape");
+
+  matmul_op->LinksTo({matmul_out});
+  transpose_op->LinksTo({transpose_out_xshape});
+  reshape_op->LinksTo({reshape_out_xshape});
+  transpose_op->LinksFrom({matmul_out}).LinksTo({transpose_out});
+  reshape_op->LinksFrom({transpose_out}).LinksTo({reshape_out});
+  return reshape_out;
+}
+
 }  // namespace ir
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/ir/graph_pattern_detector.h

@@ -1210,6 +1210,24 @@ struct DeleteQuantDequantOpPattern : public PatternBase {
   PATTERN_DECL_NODE(any_op2);
 };
 
+// Matmul + Transpose + Reshape
+struct MatmulTransposeReshapePattern : public PatternBase {
+  MatmulTransposeReshapePattern(PDPattern* pattern,
+                                const std::string& name_scope)
+      : PatternBase(pattern, name_scope, "matmul_transpose_reshape") {}
+
+  PDNode* operator()();
+
+  PATTERN_DECL_NODE(matmul_op);
+  PATTERN_DECL_NODE(matmul_out);
+  PATTERN_DECL_NODE(transpose_op);
+  PATTERN_DECL_NODE(transpose_out);
+  PATTERN_DECL_NODE(transpose_out_xshape);
+  PATTERN_DECL_NODE(reshape_op);
+  PATTERN_DECL_NODE(reshape_out);
+  PATTERN_DECL_NODE(reshape_out_xshape);
+};
+
 }  // namespace patterns
 
 // Link two ir::Nodes from each other.

paddle/fluid/framework/ir/mkldnn/matmul_transpose_reshape_fuse_pass.cc

+// Copyright (c) 2020 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.
+
+#include "paddle/fluid/framework/ir/mkldnn/matmul_transpose_reshape_fuse_pass.h"
+#include <paddle/fluid/string/pretty_log.h>
+#include <vector>
+#include "paddle/fluid/platform/enforce.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+void MatmulTransposeReshapeMKLDNNPass::ApplyImpl(ir::Graph *graph) const {
+  PADDLE_ENFORCE_NOT_NULL(graph,
+                          platform::errors::InvalidArgument(
+                              "Pointer to graph argument should not be NULL."));
+  FusePassBase::Init(name_scope_, graph);
+
+  GraphPatternDetector gpd;
+  patterns::MatmulTransposeReshapePattern mtrp(gpd.mutable_pattern(),
+                                               name_scope_);
+
+  mtrp();
+
+  int found_matmul_transpose_reshape_count = 0;
+  auto handler = [&](const GraphPatternDetector::subgraph_t &subgraph,
+                     Graph *g) {
+    VLOG(4) << "handle matmul_transpose_reshape fuse";
+    GET_IR_NODE_FROM_SUBGRAPH(matmul_op, matmul_op, mtrp);
+    GET_IR_NODE_FROM_SUBGRAPH(matmul_out, matmul_out, mtrp);
+    GET_IR_NODE_FROM_SUBGRAPH(transpose_op, transpose_op, mtrp);
+    GET_IR_NODE_FROM_SUBGRAPH(transpose_out, transpose_out, mtrp);
+    GET_IR_NODE_FROM_SUBGRAPH(transpose_out_xshape, transpose_out_xshape, mtrp);
+    GET_IR_NODE_FROM_SUBGRAPH(reshape_op, reshape_op, mtrp);
+    GET_IR_NODE_FROM_SUBGRAPH(reshape_out, reshape_out, mtrp);
+    GET_IR_NODE_FROM_SUBGRAPH(reshape_out_xshape, reshape_out_xshape, mtrp);
+    auto reshape_shape =
+        boost::get<std::vector<int>>(reshape_op->Op()->GetAttr("shape"));
+    auto transpose_axis =
+        boost::get<std::vector<int>>(transpose_op->Op()->GetAttr("axis"));
+
+    auto reshape_out_size = reshape_shape.size();
+    auto transpose_out_size = transpose_axis.size();
+    const std::vector<int> supported_axis{0, 2, 1, 3};
+    const bool supported_transpose_axis = std::equal(
+        transpose_axis.begin(), transpose_axis.end(), supported_axis.begin());
+    if (transpose_out_size != 4) {
+      VLOG(3) << "do not perform matmul_transpose_reshape fuse: "
+              << "supported rank is 4, received " << transpose_out_size;
+      return;
+    }
+    if (!supported_transpose_axis) {
+      VLOG(3) << "do not perform matmul_transpose_reshape fuse: "
+              << "supported transpose axis for the fuse are {0, 2, 1, 3}";
+      return;
+    }
+    if (reshape_out_size != 3) {
+      VLOG(3) << "do not perform matmul_transpose_reshape fuse: "
+              << "reshape_out supported rank is 3, received "
+              << reshape_out_size;
+      return;
+    }
+    OpDesc *matmul_desc = matmul_op->Op();
+    matmul_desc->SetOutput("Out", {reshape_out->Name()});
+    matmul_desc->SetAttr("fused_reshape_Out", reshape_shape);
+    matmul_desc->SetAttr("fused_transpose_Out", transpose_axis);
+
+    GraphSafeRemoveNodes(graph,
+                         {matmul_out, transpose_op, transpose_out, reshape_op,
+                          transpose_out_xshape, reshape_out_xshape});
+
+    IR_OP_VAR_LINK(matmul_op, reshape_out);
+
+    found_matmul_transpose_reshape_count++;
+  };
+
+  gpd(graph, handler);
+  AddStatis(found_matmul_transpose_reshape_count);
+  std::stringstream msg_ss;
+  msg_ss << "---    Fused " << found_matmul_transpose_reshape_count
+         << " MatmulTransposeReshape patterns";
+  paddle::string::PrettyLogDetail(msg_ss.str().c_str());
+}
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(matmul_transpose_reshape_fuse_pass,
+              paddle::framework::ir::MatmulTransposeReshapeMKLDNNPass);

paddle/fluid/framework/ir/mkldnn/matmul_transpose_reshape_fuse_pass.h

+// Copyright (c) 2020 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
+
+#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 {
+class MatmulTransposeReshapeMKLDNNPass : public FusePassBase {
+ public:
+  virtual ~MatmulTransposeReshapeMKLDNNPass() {}
+
+ protected:
+  void ApplyImpl(Graph* graph) const override;
+  const std::string name_scope_{"matmul_transpose_reshape_fuse"};
+};
+}
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/ir/mkldnn/matmul_transpose_reshape_fuse_pass_tester.cc

+// Copyright (c) 2020 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.
+
+#include "paddle/fluid/framework/ir/mkldnn/matmul_transpose_reshape_fuse_pass.h"
+#include <gtest/gtest.h>
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+void SetOp(ProgramDesc *prog, const std::string &type,
+           const std::vector<std::string> &inputs,
+           const std::vector<std::string> &outputs) {
+  auto *op = prog->MutableBlock(0)->AppendOp();
+  op->SetType(type);
+  op->SetInput("X", {inputs[0]});
+  op->SetOutput("Out", {outputs[0]});
+  if (type == "transpose2") {
+    op->SetAttr("axis", std::vector<int>({0, 2, 1, 3}));
+    op->SetOutput("XShape", {outputs[1]});
+  }
+  if (type == "reshape2") {
+    op->SetAttr("shape", std::vector<int>({4, 5, 6}));
+    op->SetOutput("XShape", {outputs[1]});
+  }
+
+  if (type == "matmul") {
+    op->SetInput("Y", {inputs[1]});
+    op->SetAttr("use_mkldnn", true);
+  }
+}
+
+ProgramDesc BuildProgramDesc() {
+  ProgramDesc prog;
+  for (auto &v : std::initializer_list<std::string>(
+           {"a1", "a2", "b", "c", "cx", "d", "dx", "e"})) {
+    auto *var = prog.MutableBlock(0)->Var(v);
+    var->SetType(proto::VarType::SELECTED_ROWS);
+  }
+
+  SetOp(&prog, "matmul", {"a1", "a2"}, {"b"});
+  SetOp(&prog, "transpose2", {"b"}, {"c", "cx"});
+  SetOp(&prog, "reshape2", {"c"}, {"d", "dx"});
+  SetOp(&prog, "fc", {"d"}, {"e"});
+
+  return prog;
+}
+
+void MainTest(const ProgramDesc &prog) {
+  std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
+
+  int original_nodes_num = graph->Nodes().size();
+
+  auto pass =
+      PassRegistry::Instance().Get("matmul_transpose_reshape_fuse_pass");
+  graph.reset(pass->Apply(graph.release()));
+
+  int current_nodes_num = graph->Nodes().size();
+  EXPECT_EQ(original_nodes_num - 6, current_nodes_num);
+
+  for (auto *node : graph->Nodes()) {
+    if (node->IsOp()) {
+      auto *op = node->Op();
+      if (op->Type() == "matmul") {
+        EXPECT_EQ(op->GetAttrIfExists<std::vector<int>>("fused_reshape_Out"),
+                  std::vector<int>({4, 5, 6}));
+        EXPECT_EQ(op->GetAttrIfExists<std::vector<int>>("fused_transpose_Out"),
+                  std::vector<int>({0, 2, 1, 3}));
+      }
+    }
+  }
+}
+
+TEST(MatmulTransposeReshapeFusePass, matmul_inputs) {
+  auto prog = BuildProgramDesc();
+  MainTest(prog);
+}
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+USE_PASS(matmul_transpose_reshape_fuse_pass);

paddle/fluid/inference/api/paddle_pass_builder.cc

@@ -196,6 +196,7 @@ void CpuPassStrategy::EnableMKLDNN() {
              "conv_relu6_mkldnn_fuse_pass",         //
              "conv_swish_mkldnn_fuse_pass",         //
              "scale_matmul_fuse_pass",              //
+             "matmul_transpose_reshape_fuse_pass",  //
              // Disabled due to topology-dependent speed-up
              // "fc_mkldnn_pass",
              "mkldnn_inplace_pass",  // This pass should be activated after

paddle/fluid/operators/matmul_op.cc

@@ -407,7 +407,45 @@ class MatMulOp : public framework::OperatorWithKernel {
     if (dim_out.empty()) {
       dim_out = {1};
     }
-    context->SetOutputDim("Out", framework::make_ddim(dim_out));
+
+    framework::DDim ddim_out = framework::make_ddim(dim_out);
+
+#ifdef PADDLE_WITH_MKLDNN
+    //  if mkldnn matmul+transpose+reshape fuse activated
+    auto reshape_out =
+        context->Attrs().Get<std::vector<int>>("fused_reshape_Out");
+    auto transpose_out =
+        context->Attrs().Get<std::vector<int>>("fused_transpose_Out");
+
+    if (!reshape_out.empty() && !transpose_out.empty()) {
+      auto reshape_out_size = reshape_out.size();
+      auto transpose_out_size = transpose_out.size();
+      PADDLE_ENFORCE_EQ(transpose_out_size, 4,
+                        platform::errors::InvalidArgument(
+                            "transpose_out supported rank is 4, "
+                            "received %d",
+                            transpose_out_size));
+      const std::vector<int> supported_axis{0, 2, 1, 3};
+      const bool supported_transpose_axis = std::equal(
+          transpose_out.begin(), transpose_out.end(), supported_axis.begin());
+      PADDLE_ENFORCE_EQ(
+          supported_transpose_axis, true,
+          platform::errors::InvalidArgument(
+              "supported transpose axis for the fuse are {0, 2, 1, 3}"));
+      PADDLE_ENFORCE_EQ(
+          reshape_out_size, 3,
+          platform::errors::InvalidArgument("reshape_out supported rank is 3, "
+                                            "received %d",
+                                            reshape_out_size));
+      framework::DDim shape_out =
+          ddim_out.transpose(transpose_out).reshape(reshape_out);
+      context->SetOutputDim("Out", shape_out);
+    } else {
+      context->SetOutputDim("Out", ddim_out);
+    }
+#else
+    context->SetOutputDim("Out", ddim_out);
+#endif
     context->ShareLoD("X", /*->*/ "Out");
   }
 
@@ -446,6 +484,16 @@ class MatMulOpMaker : public framework::OpProtoAndCheckerMaker {
         "use_mkldnn",
         "(bool, default false) Indicates if MKL-DNN kernel will be used")
         .SetDefault(false);
+    AddAttr<std::vector<int>>(
+        "fused_reshape_Out",
+        R"DOC(When MKLDNN MatMul_transpose_reshape fuse activated, "
+              "it's a shape atribute of fused reshape for `Out` output.)DOC")
+        .SetDefault({});
+    AddAttr<std::vector<int>>(
+        "fused_transpose_Out",
+        R"DOC(When MKLDNN MatMul_transpose_reshape fuse activated, "
+              "it's a axis atribute of fused transpose for `Out` output.)DOC")
+        .SetDefault({});
     /* int8 parameters */
     AddAttr<bool>("use_quantizer",
                   "(bool, default false) "
@@ -466,6 +514,7 @@ class MatMulOpMaker : public framework::OpProtoAndCheckerMaker {
                   "(bool, default false) Force INT8 kernel output FP32, only "
                   "used in MKL-DNN INT8")
         .SetDefault(false);
+
 #if defined(PADDLE_WITH_MKLML) && !defined(PADDLE_WITH_CUDA)
     AddAttr<int>("head_number", "The number of heads of the matrix")
         .SetDefault(1);

paddle/fluid/operators/mkldnn/matmul_mkldnn_op.cc

@@ -31,6 +31,11 @@ using platform::MKLDNNDeviceContext;
 using framework::ExecutionContext;
 using Tensor = framework::Tensor;
 
+template <typename T>
+constexpr bool IsInt8() {
+  return std::is_same<T, int8_t>::value || std::is_same<T, uint8_t>::value;
+}
+
 // Get row matrix shape from a vector shape. If the rank of x_dim > 1, the
 // original x_dim is returned.
 static framework::DDim RowMatrixDimsFromVector(const framework::DDim& x_dim) {
@@ -64,7 +69,8 @@ class MatMulFactory {
 
  private:
   struct MatMulDims {
-    const memory::dim BS, M, N, K;
+    const memory::dims x_dims, y_dims, out_dims, x_strides, y_strides,
+        out_strides;
   };
 
   void SetDNNLEngine(const ExecutionContext& ctx) {
@@ -80,6 +86,19 @@ class MatMulFactory {
     return dnnl::memory(md, engine_, to_void_cast(data));
   }
 
+  bool IsOutputFused(const ExecutionContext& ctx) const {
+    auto& fused_reshape_Out = ctx.Attr<std::vector<int>>("fused_reshape_Out");
+    auto& fused_transpose_Out =
+        ctx.Attr<std::vector<int>>("fused_transpose_Out");
+    return !fused_reshape_Out.empty() && !fused_transpose_Out.empty();
+  }
+
+  void CorrectStridesWhenFloatOutputFused(const ExecutionContext& ctx,
+                                          const memory::dim N, memory::dim b,
+                                          memory::dims* out_strides) const {
+    if (!IsInt8<OT>() && IsOutputFused(ctx)) *out_strides = {N, b * N, 1};
+  }
+
   MatMulDims GetMatmulDims(const ExecutionContext& ctx) {
     auto mat_dim_x = math::CreateMatrixDescriptor(
         RowMatrixDimsFromVector(ctx.Input<Tensor>("X")->dims()), 0,
@@ -100,34 +119,45 @@ class MatMulFactory {
     const memory::dim M = mat_dim_x.height_;
     const memory::dim N = mat_dim_y.width_;
     const memory::dim K = mat_dim_x.width_;
-    return {BS, M, N, K};
+
+    batch_size_ = 1;
+    auto b = BS;
+    if (BS > 1 && IsOutputFused(ctx)) {
+      batch_size_ = ctx.Input<Tensor>("X")->dims()[0];
+      b = BS / batch_size_;
+    }
+    memory::dims x_dims = {b, M, K};
+    memory::dims y_dims = {b, K, N};
+    memory::dims out_dims = {b, M, N};
+
+    size_t x_size = b * M * K * sizeof(XT);
+    size_t y_size = b * K * N * sizeof(YT);
+    size_t out_size = b * M * N * sizeof(OT);
+    offsets_ = {x_size, y_size, out_size};
+
+    // Translate transA and transB
+    memory::dims strides_x = !ctx.Attr<bool>("transpose_X")
+                                 ? memory::dims{M * K, K, 1}
+                                 : memory::dims{M * K, 1, M};
+    memory::dims strides_y = !ctx.Attr<bool>("transpose_Y")
+                                 ? memory::dims{N * K, N, 1}
+                                 : memory::dims{N * K, 1, K};
+    memory::dims out_strides = memory::dims{M * N, N, 1};
+
+    CorrectStridesWhenFloatOutputFused(ctx, N, b, &out_strides);
+
+    return {x_dims, y_dims, out_dims, strides_x, strides_y, out_strides};
   }
 
   void CreateMemories(const ExecutionContext& ctx) {
     auto matmul_dims = GetMatmulDims(ctx);
-    auto BS = matmul_dims.BS;
-    auto M = matmul_dims.M;
-    auto N = matmul_dims.N;
-    auto K = matmul_dims.K;
-    bool x_trans = ctx.Attr<bool>("transpose_X");
-    bool y_trans = ctx.Attr<bool>("transpose_Y");
-
-    typedef memory::dims dims;
-    dims x_dims = {BS, M, K};
-    dims y_dims = {BS, K, N};
-    dims out_dims = {BS, M, N};
 
-    // Translate transA and transB
-    dims x_strides = !x_trans ? dims{M * K, K, 1} : dims{M * K, 1, M};
-    dims y_strides = !y_trans ? dims{N * K, N, 1} : dims{N * K, 1, K};
-    dims out_strides = {M * N, N, 1};
-
-    x_mem_ =
-        CreateMemory<XT>(x_dims, x_strides, ctx.Input<Tensor>("X")->data<XT>());
-    y_mem_ =
-        CreateMemory<YT>(y_dims, y_strides, ctx.Input<Tensor>("Y")->data<YT>());
+    x_mem_ = CreateMemory<XT>(matmul_dims.x_dims, matmul_dims.x_strides,
+                              ctx.Input<Tensor>("X")->data<XT>());
+    y_mem_ = CreateMemory<YT>(matmul_dims.y_dims, matmul_dims.y_strides,
+                              ctx.Input<Tensor>("Y")->data<YT>());
     out_mem_ = CreateMemory<OT>(
-        out_dims, out_strides,
+        matmul_dims.out_dims, matmul_dims.out_strides,
         ctx.Output<Tensor>("Out")->mutable_data<OT>(ctx.GetPlace()));
   }
 
@@ -156,11 +186,25 @@ class MatMulFactory {
 
   void Execute() {
     dnnl::stream stream(engine_);
+
+    auto offsets = offsets_;
+    unsigned bs = batch_size_;
+    void* x_ptr = x_mem_.get_data_handle();
+    void* y_ptr = y_mem_.get_data_handle();
+    void* out_ptr = out_mem_.get_data_handle();
+    for (unsigned i = 0; i < bs; i++) {
+      x_mem_.set_data_handle(x_ptr);
+      y_mem_.set_data_handle(y_ptr);
+      out_mem_.set_data_handle(out_ptr);
       matmul_prim_.execute(stream, {
                                        {MKLDNN_ARG_SRC, x_mem_},
                                        {MKLDNN_ARG_WEIGHTS, y_mem_},
                                        {MKLDNN_ARG_DST, out_mem_},
                                    });
+      x_ptr = static_cast<char*>(x_ptr) + offsets.x_offset;
+      y_ptr = static_cast<char*>(y_ptr) + offsets.y_offset;
+      out_ptr = static_cast<char*>(out_ptr) + offsets.out_offset;
+    }
     stream.wait();
   }
 
@@ -188,11 +232,19 @@ class MatMulFactory {
   void SetInitialized() { initialized_ = true; }
 
  private:
+  struct memory_offsets {
+    size_t x_offset;
+    size_t y_offset;
+    size_t out_offset;
+  };
+
   dnnl::engine engine_;
   dnnl::memory x_mem_;
   dnnl::memory y_mem_;
   dnnl::memory out_mem_;
   dnnl::matmul matmul_prim_;
+  memory_offsets offsets_;
+  unsigned batch_size_;
   bool initialized_ = false;
 };
 
@@ -217,10 +269,6 @@ static std::shared_ptr<MatMulFactory<XT, YT, OT>> GetPrimitiveFactory(
   return factory;
 }
 
-template <typename T>
-constexpr bool IsInt8() {
-  return std::is_same<T, int8_t>::value || std::is_same<T, uint8_t>::value;
-}
 // Choose appropriate primitive factory implementation based on inferred
 // output type (uint8, int8 or float).
 template <typename XT, typename YT>

python/paddle/fluid/contrib/slim/quantization/qat2_int8_mkldnn_pass.py

@@ -371,6 +371,7 @@ class Qat2Int8MkldnnPass(object):
                                      ['use_gpu', 'use_fc_padding'],
                                      [False, False])
             graph = self._apply_pass(graph, 'fc_mkldnn_pass')
+        graph = self._apply_pass(graph, 'matmul_transpose_reshape_fuse_pass')
         return graph
 
     def _apply_pass(self, graph, pass_name, attrs=None, attr_values=None):

python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_matmul_op_output_fuse_pass.py

+# Copyright (c) 2020 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+
+import paddle.fluid as fluid
+from inference_pass_test import InferencePassTest
+
+
+class TestMKLDNNMatmulFuseOp(InferencePassTest):
+    def init_data(self):
+        self.bs = 8
+        self.d_type = np.float32
+        self.shape_x = [12, 128, 128]
+        self.shape_y = [12, 128, 64]
+        self.enable_mkldnn = True
+
+    def make_network(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            x = fluid.data(
+                name='x', shape=[-1] + self.shape_x, dtype=self.d_type)
+            y = fluid.data(
+                name='y', shape=[-1] + self.shape_y, dtype=self.d_type)
+            out = fluid.layers.matmul(x, y)
+            out = fluid.layers.transpose(out, perm=[0, 2, 1, 3])
+            out = fluid.layers.reshape(
+                out, [0, 0, self.shape_y[0] * self.shape_y[2]])
+            out = fluid.layers.fc(out, size=1)
+        return out
+
+    def setUp(self):
+        self.init_data()
+        out = self.make_network()
+        self.set_feeds(out)
+
+    def set_feeds(self, out):
+        self.feeds = {
+            "x": np.random.random([self.bs] + self.shape_x).astype(self.d_type),
+            "y": np.random.random([self.bs] + self.shape_y).astype(self.d_type)
+        }
+        self.fetch_list = [out]
+
+    def test_check_output(self):
+        use_gpu = False
+        self.check_output_with_option(use_gpu)
+
+
+class TestMKLDNNMatmulOtherDimsFuseOp(TestMKLDNNMatmulFuseOp):
+    def init_data(self):
+        self.bs = 8
+        self.d_type = np.float32
+        self.shape_x = [12, 1, 1]
+        self.shape_y = [12, 1, 64]
+        self.enable_mkldnn = True
+
+
+class TestMKLDNNMatmulOpNotFusedWrongTransposeAxis(TestMKLDNNMatmulFuseOp):
+    def make_network(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            x = fluid.data(
+                name='x', shape=[-1] + self.shape_x, dtype=self.d_type)
+            y = fluid.data(
+                name='y', shape=[-1] + self.shape_y, dtype=self.d_type)
+            out = fluid.layers.matmul(x, y)
+            out = fluid.layers.transpose(out, perm=[0, 1, 2, 3])
+            out = fluid.layers.reshape(out, [0, 0, 0, 0])
+            out = fluid.layers.fc(out, size=1)
+        return out
+
+
+class TestMKLDNNMatmulOpNotFusedBreakPattern(TestMKLDNNMatmulFuseOp):
+    def init_data(self):
+        self.bs = 7
+        self.d_type = np.float32
+        self.shape_x = [12, 128, 128]
+        self.shape_y = [12, 128, 64]
+        self.enable_mkldnn = True
+
+    def make_network(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            x = fluid.data(
+                name='x', shape=[-1] + self.shape_x, dtype=self.d_type)
+            y = fluid.data(
+                name='y', shape=[-1] + self.shape_y, dtype=self.d_type)
+            out = fluid.layers.matmul(x, y)
+            out = fluid.layers.transpose(out, perm=[0, 2, 1, 3])
+            out = fluid.layers.transpose(
+                out, perm=[0, 1, 2, 3])  # breaks pattern
+            out = fluid.layers.reshape(
+                out, [0, 0, self.shape_y[0] * self.shape_y[2]])
+            out = fluid.layers.fc(out, size=1)
+        return out
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/mkldnn/test_matmul_mkldnn_op.py

@@ -161,5 +161,134 @@ class TestDnnlMatMulOpInt8ForceFP32BasicScales(TestDnnlMatMulOp):
         self.attrs = {'force_fp32_output': True}
 
 
+@skip_check_grad_ci(reason="Tests inference only optimization.")
+class TestMatMulOpTransposeReshapeEmptyFloat(OpTest):
+    def init_data_type(self):
+        self.data_type_ = np.float32
+
+    def generate_data(self):
+        self.bs = 1
+        self.x = np.random.random([self.bs, 128, 128]).astype(self.data_type_)
+        self.y = np.random.random([self.bs, 128, 64]).astype(self.data_type_)
+
+    def init_params_and_out(self):
+        self.transpose_out = []
+        self.reshape_out = []
+        self.out = np.matmul(self.x, self.y)
+
+    def setUp(self):
+        os.environ["DNNL_MAX_CPU_ISA"] = "AVX"
+        self.op_type = "matmul"
+        self._cpu_only = True
+        self.use_mkldnn = True
+        self.init_data_type()
+        self.generate_data()
+        self.init_params_and_out()
+
+        self.inputs = {'X': self.x, 'Y': self.y}
+        self.attrs = {'use_mkldnn': self.use_mkldnn}
+
+        if len(self.reshape_out) > 0:
+            self.attrs['fused_reshape_Out'] = self.reshape_out
+        if len(self.transpose_out) > 0:
+            self.attrs['fused_transpose_Out'] = self.transpose_out
+
+        self.inputs = {'X': self.x, 'Y': self.y}
+        self.outputs = {'Out': self.out}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def check_raise_error(self, msg):
+        try:
+            self.check_output()
+        except Exception as e:
+            if msg in str(e):
+                raise AttributeError
+            else:
+                print(e)
+
+
+class TestMatMulOpTransposeReshapeIntEmptyInt(
+        TestMatMulOpTransposeReshapeEmptyFloat):
+    def init_data_type(self):
+        self.data_type_ = np.int8
+
+
+class TestMatMulOpTransposeReshapeBasicFloat(
+        TestMatMulOpTransposeReshapeEmptyFloat):
+    def generate_data(self):
+        self.bs = 8
+        self.x = np.random.random(
+            [self.bs, 12, 128, 128]).astype(self.data_type_)
+        self.y = np.random.random(
+            [self.bs, 12, 128, 64]).astype(self.data_type_)
+
+    def init_params_and_out(self):
+        self.transpose_out = [0, 2, 1, 3]
+        self.reshape_out = [0, 0, self.x.shape[1] * self.y.shape[-1]]
+        self.out = np.matmul(self.x, self.y).transpose([0, 2, 1, 3]).reshape(
+            [self.bs, -1, self.x.shape[1] * self.y.shape[-1]])
+
+
+class TestMatMulOpTransposeReshapeBasicInt(
+        TestMatMulOpTransposeReshapeBasicFloat):
+    def init_data_type(self):
+        self.data_type_ = np.int8
+
+
+class TestMatMulOpTransposeReshapeOtherDimFloat(
+        TestMatMulOpTransposeReshapeBasicFloat):
+    def generate_data(self):
+        self.bs = 11
+        self.x = np.random.random([self.bs, 12, 14, 18]).astype(self.data_type_)
+        self.y = np.random.random([self.bs, 12, 18, 13]).astype(self.data_type_)
+
+
+class TestMatMulOpTransposeReshapeOtherDimInt(
+        TestMatMulOpTransposeReshapeOtherDimFloat):
+    def init_data_type(self):
+        self.data_type_ = np.int8
+
+
+class TestMatMulOpTransposeReshapeTransposeAxisNotSupportedException(
+        TestMatMulOpTransposeReshapeBasicFloat):
+    def init_params_and_out(self):
+        self.transpose_out = [0, 1, 2, 3]
+        self.reshape_out = [0, 0, self.x.shape[1] * self.y.shape[-1]]
+        self.out = np.matmul(self.x, self.y)
+
+    def test_check_output(self):
+        self.assertRaises(AttributeError, self.check_raise_error,
+                          'InvalidArgumentError: supported transpose axis '
+                          'for the fuse are {0, 2, 1, 3}')
+
+
+class TestMatMulOpTransposeReshapeTransposeRankNotSupportedException(
+        TestMatMulOpTransposeReshapeBasicFloat):
+    def init_params_and_out(self):
+        self.transpose_out = [0, 2, 1]
+        self.reshape_out = [0, 0, self.x.shape[1] * self.y.shape[-1]]
+        self.out = np.matmul(self.x, self.y)
+
+    def test_check_output(self):
+        self.assertRaises(
+            AttributeError, self.check_raise_error,
+            'InvalidArgumentError: transpose_out supported rank is 4')
+
+
+class TestMatMulOpTransposeReshapeRankOfReshapeNotSupportedException(
+        TestMatMulOpTransposeReshapeBasicFloat):
+    def init_params_and_out(self):
+        self.transpose_out = [0, 2, 1, 3]
+        self.reshape_out = [0, 0]
+        self.out = np.matmul(self.x, self.y)
+
+    def test_check_output(self):
+        self.assertRaises(
+            AttributeError, self.check_raise_error,
+            'InvalidArgumentError: reshape_out supported rank is 3')
+
+
 if __name__ == "__main__":
     unittest.main()