add reshape+transpose+matmul_v2 only (#37847)

* reshape+transpose+matmul_v2

* in_name->input_name

* fix pr-ci-static-check

paddle/fluid/framework/ir/CMakeLists.txt

@@ -123,6 +123,7 @@ if(WITH_MKLDNN)
     pass_library(cpu_quantize_pass inference DIR mkldnn)
     pass_library(cpu_quantize_squash_pass inference DIR mkldnn)
     pass_library(reshape_transpose_matmul_mkldnn_fuse_pass inference DIR mkldnn)
+    pass_library(reshape_transpose_matmul_v2_mkldnn_fuse_pass inference DIR mkldnn)
     pass_library(matmul_transpose_reshape_fuse_pass inference DIR mkldnn)
     pass_library(matmul_v2_transpose_reshape_fuse_pass inference DIR mkldnn)
     pass_library(batch_norm_act_fuse_pass inference DIR mkldnn)
@@ -190,7 +191,7 @@ endif()
     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_reshape_transpose_matmul_mkldnn_fuse_pass SRCS mkldnn/reshape_transpose_matmul_mkldnn_fuse_pass_tester.cc DEPS reshape_transpose_matmul_mkldnn_fuse_pass)
+    cc_test(test_reshape_transpose_matmul_mkldnn_fuse_pass SRCS mkldnn/reshape_transpose_matmul_mkldnn_fuse_pass_tester.cc DEPS reshape_transpose_matmul_mkldnn_fuse_pass reshape_transpose_matmul_v2_mkldnn_fuse_pass)
     cc_test(test_matmul_transpose_reshape_fuse_pass SRCS mkldnn/matmul_transpose_reshape_fuse_pass_tester.cc DEPS matmul_transpose_reshape_fuse_pass matmul_v2_transpose_reshape_fuse_pass)
     cc_test(test_cpu_bfloat16_placement_pass SRCS mkldnn/cpu_bfloat16_placement_pass_tester.cc DEPS cpu_bfloat16_placement_pass)
     cc_test(test_cpu_bfloat16_pass SRCS mkldnn/cpu_bfloat16_pass_tester.cc DEPS cpu_bfloat16_pass)

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -2711,12 +2711,13 @@ void patterns::DeleteQuantDequantFilterOpPattern::operator()() {
 }
 
 PDNode *patterns::ReshapeTransposeMatmulPattern::operator()(
-    bool with_reshape_xshape, bool with_transpose_xshape) {
+    const std::string &op_name, bool with_reshape_xshape,
+    bool with_transpose_xshape) {
   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_op = pattern->NewNode(matmul_op_repr())->assert_is_op(op_name);
 
   auto reshape_in = pattern->NewNode(reshape_in_repr())
                         ->AsInput()
@@ -2737,7 +2738,7 @@ PDNode *patterns::ReshapeTransposeMatmulPattern::operator()(
 
   auto transpose_out = pattern->NewNode(transpose_out_repr())
                            ->AsIntermediate()
-                           ->assert_is_op_input("matmul")
+                           ->assert_is_op_input(op_name)
                            ->assert_is_op_output("transpose2", "Out");
   if (!with_transpose_xshape)
     transpose_out->assert_is_only_output_of_op("transpose2");
@@ -2751,7 +2752,7 @@ PDNode *patterns::ReshapeTransposeMatmulPattern::operator()(
 
   auto matmul_out = pattern->NewNode(matmul_out_repr())
                         ->AsOutput()
-                        ->assert_is_op_output("matmul", "Out");
+                        ->assert_is_op_output(op_name, "Out");
 
   reshape_op->LinksFrom({reshape_in}).LinksTo({reshape_out});
   if (with_reshape_xshape) reshape_op->LinksTo({reshape_xshape});

paddle/fluid/framework/ir/graph_pattern_detector.h

@@ -1570,7 +1570,8 @@ struct ReshapeTransposeMatmulPattern : public PatternBase {
                                 const std::string& name_scope)
       : PatternBase(pattern, name_scope, "reshape_transpose_matmul") {}
 
-  PDNode* operator()(bool with_reshape_xshape, bool with_transpose_xshape);
+  PDNode* operator()(const std::string& op_name, bool with_reshape_xshape,
+                     bool with_transpose_xshape);
 
   PATTERN_DECL_NODE(reshape_in);
   PATTERN_DECL_NODE(reshape_op);

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

@@ -24,6 +24,8 @@ namespace framework {
 namespace ir {
 
 ReshapeTransposeMatmulMkldnnFusePass::ReshapeTransposeMatmulMkldnnFusePass() {
+  op_name_ = "matmul";
+
   AddOpCompat(OpCompat("reshape2"))
       .AddInput("X")
       .IsTensor()
@@ -55,7 +57,7 @@ ReshapeTransposeMatmulMkldnnFusePass::ReshapeTransposeMatmulMkldnnFusePass() {
       .IsType<std::vector<int>>()
       .End();
 
-  AddOpCompat(OpCompat("matmul"))
+  AddOpCompat(OpCompat(op_name_))
       .AddInput("X")
       .IsTensor()
       .End()
@@ -82,17 +84,17 @@ void ReshapeTransposeMatmulMkldnnFusePass::Fuse(
   patterns::ReshapeTransposeMatmulPattern rtm_pattern(gpd.mutable_pattern(),
                                                       name_scope_);
 
-  rtm_pattern(with_reshape_xshape, with_transpose_xshape);
+  rtm_pattern(op_name_, with_reshape_xshape, with_transpose_xshape);
 
   int found_reshape_transpose_matmul_count = 0;
   auto handler = [&](const GraphPatternDetector::subgraph_t &subgraph,
                      Graph *g) {
     if (!IsCompat(subgraph, g)) {
-      LOG(WARNING) << "Op compatible check in "
-                      "reshape_transpose_matmul_mkldnn_fuse_pass failed.";
+      LOG(WARNING) << "Op compatible check in reshape_transpose_" << op_name_
+                   << "_mkldnn_fuse_pass failed.";
       return;
     }
-    VLOG(4) << "handle ReshapeTransposeMatmulMkldnn fuse";
+    VLOG(4) << "handle reshape_transpose_" << op_name_ << " fuse";
     GET_IR_NODE_FROM_SUBGRAPH(reshape_in, reshape_in, rtm_pattern);
     GET_IR_NODE_FROM_SUBGRAPH(reshape_op, reshape_op, rtm_pattern);
     GET_IR_NODE_FROM_SUBGRAPH(reshape_out, reshape_out, rtm_pattern);
@@ -131,8 +133,8 @@ void ReshapeTransposeMatmulMkldnnFusePass::Fuse(
     } else if (matmul_desc->Inputs().at("Y").at(0) == input_var_name) {
       UpdateMatmul("Y");
     } else {
-      throw platform::errors::InvalidArgument(
-          "Unexpected input to MatMul encountered.");
+      throw platform::errors::InvalidArgument("Unexpected input to " +
+                                              op_name_ + " encountered.");
     }
 
     std::unordered_set<const ir::Node *> nodes_to_remove{
@@ -151,7 +153,7 @@ void ReshapeTransposeMatmulMkldnnFusePass::Fuse(
   if (!Has("disable_logs") || !Get<bool>("disable_logs")) {
     std::stringstream msg_ss;
     msg_ss << "---    Fused " << found_reshape_transpose_matmul_count
-           << " ReshapeTransposeMatmulMkldnn patterns";
+           << " ReshapeTransposeMatmul patterns for " << op_name_ << " Op";
     if (with_reshape_xshape) msg_ss << " with reshape's xshape";
     if (with_transpose_xshape) msg_ss << " with transpose's xshape";
     string::PrettyLogDetail(msg_ss.str().c_str());

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

@@ -28,6 +28,7 @@ namespace ir {
 class ReshapeTransposeMatmulMkldnnFusePass : public FusePassBase {
  public:
   ReshapeTransposeMatmulMkldnnFusePass();
+  virtual ~ReshapeTransposeMatmulMkldnnFusePass() {}
 
  protected:
   void ApplyImpl(ir::Graph* graph) const override;
@@ -35,6 +36,7 @@ class ReshapeTransposeMatmulMkldnnFusePass : public FusePassBase {
 
   void Fuse(Graph* graph, bool with_reshape_xshape,
             bool with_transpose_xshape) const;
+  std::string op_name_;
 };
 }  // namespace ir
 }  // namespace framework

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

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #include "paddle/fluid/framework/ir/mkldnn/reshape_transpose_matmul_mkldnn_fuse_pass.h"
+#include "paddle/fluid/framework/ir/mkldnn/reshape_transpose_matmul_v2_mkldnn_fuse_pass.h"
 
 #include <gtest/gtest.h>
 #include "paddle/fluid/framework/ir/pass_tester_helper.h"
@@ -37,7 +38,7 @@ Scope* CreateParamScope() {
   return param_scope;
 }
 
-void TestMain(bool with_xshapes) {
+void TestMain(const std::string& op_name, bool with_xshapes) {
   // inputs          operator          output
   // -----------------------------------------------
   //  a1,w1,bias1      fc          ->    b1
@@ -46,7 +47,7 @@ void TestMain(bool with_xshapes) {
   //  a2,w2,bias2      fc          ->    b2
   //  b2             reshape       ->    c2
   //  c2            transpose      ->    d2
-  // (d1, d2)        matmul        ->    (...)
+  // (d1, d2)        matmul(_v2)   ->    (...)
   Layers layers;
   auto* a1 = layers.data("a1", {-1, 128, 768});
   auto* w1 = layers.data("w1", {768, 768}, true);
@@ -66,7 +67,11 @@ void TestMain(bool with_xshapes) {
   c2->SetShape({-1, 128, 12, 64});
   auto* d2 = layers.transpose2(c2, {0, 2, 1, 3});
   d2->SetShape({-1, 12, 128, 64});
-  layers.matmul(d1, d2);
+  if (op_name == "matmul_v2") {
+    layers.matmul_v2(d1, d2);
+  } else {
+    layers.matmul(d1, d2);
+  }
 
   std::unique_ptr<ir::Graph> graph(new ir::Graph(layers.main_program()));
   graph->Set("__param_scope__", CreateParamScope());
@@ -76,8 +81,8 @@ void TestMain(bool with_xshapes) {
   int total_nodes_before = graph->Nodes().size();
   VLOG(3) << DebugString(graph);
 
-  auto pass =
-      PassRegistry::Instance().Get("reshape_transpose_matmul_mkldnn_fuse_pass");
+  auto pass = PassRegistry::Instance().Get("reshape_transpose_" + op_name +
+                                           "_mkldnn_fuse_pass");
   graph.reset(pass->Apply(graph.release()));
 
   int num_reshape_nodes_after = GetNumOpNodes(graph, "reshape2");
@@ -92,7 +97,7 @@ void TestMain(bool with_xshapes) {
   int removed = 8;  // 2* reshape, reshape_out, transpose, transpose_out
   if (with_xshapes) removed += 2;  // transpose_xshape, reshape_xshape
   EXPECT_EQ(total_nodes_before - removed, total_nodes_after);
-  auto* matmul_op_desc = GetOpNodes(graph, "matmul").at(0)->Op();
+  auto* matmul_op_desc = GetOpNodes(graph, op_name).at(0)->Op();
 
   auto check = [&matmul_op_desc](std::string a) {
     std::string shape_str = "fused_reshape_" + a;
@@ -108,12 +113,22 @@ void TestMain(bool with_xshapes) {
 
 TEST(ReshapeTransposeMatmulMkldnnFusePass,
      both_matmul_inputs_reshape_transpose) {
-  TestMain(false);
+  TestMain("matmul", false);
 }
 
 TEST(ReshapeTransposeMatmulMkldnnFusePass,
      both_matmul_inputs_reshape_transpose_one_with_xshapes) {
-  TestMain(true);
+  TestMain("matmul", true);
+}
+
+TEST(ReshapeTransposeMatmulV2MkldnnFusePass,
+     both_matmulv2_inputs_reshape_transpose) {
+  TestMain("matmul_v2", false);
+}
+
+TEST(ReshapeTransposeMatmulV2MkldnnFusePass,
+     both_matmulv2_inputs_reshape_transpose_one_with_xshapes) {
+  TestMain("matmul_v2", true);
 }
 
 }  // namespace ir
@@ -121,3 +136,4 @@ TEST(ReshapeTransposeMatmulMkldnnFusePass,
 }  // namespace paddle
 
 USE_PASS(reshape_transpose_matmul_mkldnn_fuse_pass);
+USE_PASS(reshape_transpose_matmul_v2_mkldnn_fuse_pass);

paddle/fluid/framework/ir/mkldnn/reshape_transpose_matmul_v2_mkldnn_fuse_pass.cc

+// Copyright (c) 2021 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/reshape_transpose_matmul_v2_mkldnn_fuse_pass.h"
+#include <string>
+#include <unordered_set>
+#include <vector>
+#include "paddle/fluid/framework/op_version_registry.h"
+#include "paddle/fluid/platform/enforce.h"
+#include "paddle/fluid/string/pretty_log.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+ReshapeTransposeMatmulV2MkldnnFusePass::
+    ReshapeTransposeMatmulV2MkldnnFusePass() {
+  op_name_ = "matmul_v2";
+
+  AddOpCompat(OpCompat("reshape2"))
+      .AddInput("X")
+      .IsTensor()
+      .End()
+      // The reshape2 op for this pass should not have "Shape" and "ShapeTensor"
+      .AddOutput("Out")
+      .IsTensor()
+      .End()
+      .AddOutput("XShape")
+      .IsOptional()
+      .IsTensor()
+      .End()
+      .AddAttr("shape")
+      .IsType<std::vector<int>>()
+      .End();
+
+  AddOpCompat(OpCompat("transpose2"))
+      .AddInput("X")
+      .IsTensor()
+      .End()
+      .AddOutput("Out")
+      .IsTensor()
+      .End()
+      .AddOutput("XShape")
+      .IsOptional()
+      .IsTensor()
+      .End()
+      .AddAttr("axis")
+      .IsType<std::vector<int>>()
+      .End();
+
+  AddOpCompat(OpCompat(op_name_))
+      .AddInput("X")
+      .IsTensor()
+      .End()
+      .AddInput("Y")
+      .IsTensor()
+      .End()
+      .AddOutput("Out")
+      .IsTensor()
+      .End()
+      .AddAttr("trans_x")
+      .IsType<bool>()
+      .End()
+      .AddAttr("trans_y")
+      .IsType<bool>()
+      .End();
+}
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(reshape_transpose_matmul_v2_mkldnn_fuse_pass,
+              paddle::framework::ir::ReshapeTransposeMatmulV2MkldnnFusePass);
+
+REGISTER_PASS_CAPABILITY(reshape_transpose_matmul_v2_mkldnn_fuse_pass)
+    .AddCombination(
+        paddle::framework::compatible::OpVersionComparatorCombination()
+            .EQ("matmul_v2", 0)
+            .EQ("transpose2", 0)
+            .EQ("reshape2", 0));

paddle/fluid/framework/ir/mkldnn/reshape_transpose_matmul_v2_mkldnn_fuse_pass.h

+// Copyright (c) 2021 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/mkldnn/reshape_transpose_matmul_mkldnn_fuse_pass.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+/*
+ * Fuse Reshape->Transpose->MatMulV2 when MatMulV2 uses mkldnn.
+ */
+
+class ReshapeTransposeMatmulV2MkldnnFusePass
+    : public ReshapeTransposeMatmulMkldnnFusePass {
+ public:
+  ReshapeTransposeMatmulV2MkldnnFusePass();
+  virtual ~ReshapeTransposeMatmulV2MkldnnFusePass() {}
+
+ protected:
+  const std::string name_scope_{"reshape_transpose_matmul_v2_fuse"};
+};
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/ir/pass_tester_helper.h

@@ -307,6 +307,19 @@ struct Layers {
     return out;
   }
 
+  VarDesc* matmul_v2(VarDesc* x, VarDesc* y, VarDesc* alpha = nullptr,
+                     bool trans_x = false, bool trans_y = false) {
+    VarDesc* out = lod_tensor(unique_name());
+    OpDesc* op = program_.MutableBlock(0)->AppendOp();
+    op->SetType("matmul_v2");
+    op->SetInput("X", {x->Name()});
+    op->SetInput("Y", {y->Name()});
+    op->SetOutput("Out", {out->Name()});
+    op->SetAttr("trans_x", trans_x);
+    op->SetAttr("trans_y", trans_y);
+    return out;
+  }
+
   VarDesc* transpose2(VarDesc* x, std::vector<int> axis,
                       bool with_xshape = false) {
     VarDesc* out = lod_tensor(unique_name());

paddle/fluid/inference/api/paddle_pass_builder.cc

@@ -244,16 +244,17 @@ void CpuPassStrategy::EnableMKLDNN() {
              "conv3d_bias_mkldnn_fuse_pass",  //
              "conv_elementwise_add_mkldnn_fuse_pass",
              "conv_concat_relu_mkldnn_fuse_pass",
-             "conv_relu_mkldnn_fuse_pass",                 //
-             "conv_leaky_relu_mkldnn_fuse_pass",           //
-             "conv_relu6_mkldnn_fuse_pass",                //
-             "conv_swish_mkldnn_fuse_pass",                //
-             "conv_hard_swish_mkldnn_fuse_pass",           //
-             "conv_hard_sigmoid_mkldnn_fuse_pass",         //
-             "scale_matmul_fuse_pass",                     //
-             "reshape_transpose_matmul_mkldnn_fuse_pass",  //
-             "matmul_transpose_reshape_fuse_pass",         //
-             "matmul_v2_transpose_reshape_fuse_pass",      //
+             "conv_relu_mkldnn_fuse_pass",                    //
+             "conv_leaky_relu_mkldnn_fuse_pass",              //
+             "conv_relu6_mkldnn_fuse_pass",                   //
+             "conv_swish_mkldnn_fuse_pass",                   //
+             "conv_hard_swish_mkldnn_fuse_pass",              //
+             "conv_hard_sigmoid_mkldnn_fuse_pass",            //
+             "scale_matmul_fuse_pass",                        //
+             "reshape_transpose_matmul_mkldnn_fuse_pass",     //
+             "reshape_transpose_matmul_v2_mkldnn_fuse_pass",  //
+             "matmul_transpose_reshape_fuse_pass",            //
+             "matmul_v2_transpose_reshape_fuse_pass",         //
              // Disabled due to topology-dependent speed-up
              // "fc_mkldnn_pass",
              // "fc_act_mkldnn_fuse_pass",

paddle/fluid/operators/compat/matmul_v2.pbtxt

@@ -39,6 +39,22 @@ extra {
     name: "op_device"
     type: STRING
   }
+  attrs {
+    name: "fused_reshape_X"
+    type: INTS
+  }
+  attrs {
+    name: "fused_reshape_Y"
+    type: INTS
+  }
+  attrs {
+    name: "fused_transpose_X"
+    type: INTS
+  }
+  attrs {
+    name: "fused_transpose_Y"
+    type: INTS
+  }
   attrs {
     name: "fused_reshape_Out"
     type: INTS

paddle/fluid/operators/matmul_v2_op.cc

@@ -19,6 +19,81 @@
 namespace paddle {
 namespace operators {
 
+static framework::DDim GetDimForInput(const framework::InferShapeContext& ctx,
+                                      const std::string input_name) {
+  auto shape = ctx.Attrs().Get<std::vector<int>>("fused_reshape_" + input_name);
+  auto axis =
+      ctx.Attrs().Get<std::vector<int>>("fused_transpose_" + input_name);
+  auto dim = ctx.GetInputDim(input_name);
+
+  PADDLE_ENFORCE_GT(dim.size(), 0,
+                    platform::errors::InvalidArgument(
+                        "The Input(%s) has not been initialized properly. The "
+                        "shape of Input(%s) = [%s].",
+                        dim));
+
+  // if mkldnn reshape+transpose+matmul fuse activated
+  if (!shape.empty() && !axis.empty()) {
+    PADDLE_ENFORCE_GE(
+        shape.size(), 2,
+        platform::errors::InvalidArgument(
+            "shape_%s attribute of MatMulOp was implemented for 2, 3 "
+            "or 4 dimensions.",
+            input_name));
+    PADDLE_ENFORCE_LE(
+        shape.size(), 4,
+        platform::errors::InvalidArgument(
+            "shape_%s attribute of MatMulOp was implemented for 2, 3 "
+            "or 4 dimensions.",
+            input_name));
+    PADDLE_ENFORCE_EQ(
+        shape.size(), axis.size(),
+        platform::errors::InvalidArgument(
+            "Ranks of shape_%s and axis_%s attributes of MatMulOp "
+            "must be equal.",
+            input_name, input_name));
+
+    int num_negative = std::count(shape.begin(), shape.end(), -1);
+    PADDLE_ENFORCE_LE(num_negative, 1,
+                      platform::errors::InvalidArgument(
+                          "The max number of -1 in fused_reshape_%s is 1 "
+                          "but received %d.",
+                          input_name, num_negative));
+
+    auto it_zero = std::find(shape.begin(), shape.end(), 0);
+    if (it_zero != shape.end()) {
+      for (uint64_t i = 0; i < shape.size(); i++) {
+        if (shape[i] == 0) {
+          PADDLE_ENFORCE_LT(i, dim.size(),
+                            platform::errors::InvalidArgument(
+                                "The index of 0 in fused_reshape_%s ",
+                                "should be less than output dim size, ",
+                                "but the index is %d and output dim size is %d",
+                                input_name, i, dim.size()));
+          shape[i] = dim.at(i);
+        }
+      }
+    }
+
+    // if "-1" is present then one of reshape dims must be infered
+    auto it_negative = std::find(shape.begin(), shape.end(), -1);
+    if (it_negative != shape.end()) {
+      int64_t dim_product = 1;
+      for (int i = 0; i < dim.size(); i++) {
+        dim_product *= dim.at(i);
+      }
+
+      int64_t shape_product = std::accumulate(shape.begin(), shape.end(), -1,
+                                              std::multiplies<int>());
+      int index = std::distance(shape.begin(), it_negative);
+      shape[index] = dim_product / shape_product;
+    }
+
+    dim = dim.reshape(shape).transpose(axis);
+  }
+  return dim;
+}
+
 class MatMulV2Op : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -30,9 +105,9 @@ class MatMulV2Op : public framework::OperatorWithKernel {
     bool trans_y = ctx->Attrs().Get<bool>("trans_y");
 
     std::vector<int64_t> dims_x =
-        paddle::framework::vectorize(ctx->GetInputDim("X"));
+        framework::vectorize(GetDimForInput(*ctx, "X"));
     std::vector<int64_t> dims_y =
-        paddle::framework::vectorize(ctx->GetInputDim("Y"));
+        framework::vectorize(GetDimForInput(*ctx, "Y"));
     auto ndims_x = dims_x.size();
     auto ndims_y = dims_y.size();
     PADDLE_ENFORCE_GT(ndims_x, 0,
@@ -215,6 +290,22 @@ class MatMulV2OpMaker : public framework::OpProtoAndCheckerMaker {
         .SetDefault("float32")
         .InEnum({"float32", "bfloat16"})
         .AsExtra();
+    AddAttr<std::vector<int>>("fused_reshape_X",
+                              R"DOC(Shape of fused reshape of `X` input.)DOC")
+        .SetDefault({})
+        .AsExtra();
+    AddAttr<std::vector<int>>("fused_reshape_Y",
+                              R"DOC(Shape of fused reshape of `Y` input.)DOC")
+        .SetDefault({})
+        .AsExtra();
+    AddAttr<std::vector<int>>("fused_transpose_X",
+                              R"DOC(Axis of fused transpose of `X` input.)DOC")
+        .SetDefault({})
+        .AsExtra();
+    AddAttr<std::vector<int>>("fused_transpose_Y",
+                              R"DOC(Axis of fused transpose of `Y` input.)DOC")
+        .SetDefault({})
+        .AsExtra();
     AddComment(
         R"DOC(Matrix multiplication Out = X * Y. A has shape (d0, d1 ... M, K), 
         B has shape (d0, d1 ... K, N), Out has shape ((d0, d1 ... M, N)). 

paddle/fluid/operators/mkldnn/matmul_mkldnn_op.h

@@ -12,6 +12,8 @@ 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 "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/tensor.h"
 #include "paddle/fluid/operators/math/blas.h"

paddle/fluid/operators/mkldnn/matmul_v2_mkldnn_op.cc

@@ -25,10 +25,88 @@ using paddle::platform::MKLDNNDeviceContext;
 using paddle::platform::MKLDNNGetDataType;
 using paddle::platform::to_void_cast;
 using Tensor = paddle::framework::Tensor;
+using paddle::framework::DDim;
 using paddle::framework::GradVarName;
 using paddle::framework::make_ddim;
 using paddle::framework::vectorize;
 
+// Get row matrix shape from a vector shape. If the rank of x_dim > 1, the
+// original x_dim is returned.
+static DDim RowMatrixDimsFromVector(const DDim& x_dim) {
+  return x_dim.size() > 1 ? x_dim : paddle::framework::make_ddim({1, x_dim[0]});
+}
+
+// Get column matrix shape from a vector shape. If the ran of y_dim > 1, the
+// original y_dim is returned.
+static DDim ColumnMatrixDimsFromVector(const DDim& y_dim) {
+  return y_dim.size() > 1 ? y_dim : paddle::framework::make_ddim({y_dim[0], 1});
+}
+
+static std::vector<int64_t> Transpose(const std::vector<int64_t>& x,
+                                      const std::vector<int>& axis) {
+  size_t in_rank = x.size();
+  size_t axis_size = axis.size();
+
+  auto axis_set = std::set<int>(axis.begin(), axis.end());
+  PADDLE_ENFORCE_EQ(axis_set.size(), axis_size,
+                    paddle::platform::errors::InvalidArgument(
+                        "In an axis array, elements must be unique."));
+
+  PADDLE_ENFORCE_EQ(in_rank, axis_size,
+                    paddle::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));
+
+  PADDLE_ENFORCE_LT(*std::max_element(axis.begin(), axis.end()), axis_size,
+                    paddle::platform::errors::InvalidArgument(
+                        "Axis values must be ranging from 0 to (dims - 1)."));
+
+  std::vector<int64_t> new_x(x.size());
+  for (size_t i = 0; i < x.size(); i++) {
+    new_x[i] = x[axis[i]];
+  }
+  return new_x;
+}
+
+std::vector<int64_t> GetInputStrides(const ExecutionContext& ctx,
+                                     const std::string input_name) {
+  auto shape = ctx.Attr<std::vector<int>>("fused_reshape_" + input_name);
+  auto axis = ctx.Attr<std::vector<int>>("fused_transpose_" + input_name);
+  auto input_dims = ctx.Input<Tensor>(input_name)->dims();
+  auto new_dims = input_dims;
+  if (!shape.empty() && !axis.empty()) {
+    new_dims = input_dims.reshape(shape).transpose(axis);
+  }
+
+  auto& MatrixDimsFromVector =
+      input_name == "X" ? RowMatrixDimsFromVector : ColumnMatrixDimsFromVector;
+  paddle::operators::math::MatDescriptor mat_dim =
+      paddle::operators::math::CreateMatrixDescriptor(
+          MatrixDimsFromVector(new_dims), 0,
+          ctx.Attr<bool>(std::string("trans_") +
+                         static_cast<char>(std::tolower(input_name[0]))));
+
+  std::vector<int64_t> strides;
+  if (!shape.empty()) {
+    auto shape2 = input_dims.reshape(shape);
+    strides.push_back(1);
+    for (auto i = shape2.size() - 1; i > 0; --i) {
+      strides.insert(strides.begin(),
+                     strides.front() * static_cast<int64_t>(shape2[i]));
+    }
+    strides = Transpose(strides, axis);
+    if (shape.size() == 2)
+      strides.insert(strides.begin(),
+                     static_cast<int64_t>(shape[0] * shape[1]));
+    mat_dim.stride_ = strides[0];
+    if (mat_dim.trans_) std::swap(*strides.rbegin(), *(++strides.rbegin()));
+  }
+  return strides;
+}
+
 template <typename T>
 class MatMulV2MKLDNNHandler
     : public paddle::platform::MKLDNNHandlerNoCachingT<T, dnnl::matmul> {
@@ -37,7 +115,9 @@ class MatMulV2MKLDNNHandler
                         paddle::platform::Place cpu_place,
                         const std::vector<int64_t>& x_org_dims, bool trans_x,
                         const std::vector<int64_t>& y_org_dims, bool trans_y,
-                        bool is_output_fused)
+                        bool is_output_fused,
+                        const std::vector<int64_t>& x_strides_override,
+                        const std::vector<int64_t>& y_strides_override)
       : paddle::platform::MKLDNNHandlerNoCachingT<T, dnnl::matmul>(engine,
                                                                    cpu_place) {
     // M X K * K X N
@@ -64,16 +144,24 @@ class MatMulV2MKLDNNHandler
     y_strides.reserve(x_dims.size());
     out_strides.reserve(x_dims.size());
 
-    if (!trans_x) {
-      x_strides.insert(x_strides.end(), {M * K, K, 1});
+    if (!x_strides_override.empty()) {
+      x_strides = x_strides_override;
     } else {
-      x_strides.insert(x_strides.end(), {M * K, 1, M});
+      if (!trans_x) {
+        x_strides.insert(x_strides.end(), {M * K, K, 1});
+      } else {
+        x_strides.insert(x_strides.end(), {M * K, 1, M});
+      }
     }
 
-    if (!trans_y) {
-      y_strides.insert(y_strides.end(), {N * K, N, 1});
+    if (!y_strides_override.empty()) {
+      y_strides = y_strides_override;
     } else {
-      y_strides.insert(y_strides.end(), {N * K, 1, K});
+      if (!trans_y) {
+        y_strides.insert(y_strides.end(), {N * K, N, 1});
+      } else {
+        y_strides.insert(y_strides.end(), {N * K, 1, K});
+      }
     }
 
     out_strides.insert(out_strides.end(), {M * N, N, 1});
@@ -82,8 +170,12 @@ class MatMulV2MKLDNNHandler
 
     for (int i = x_dims.size() - 4; i >= 0; --i) {
       out_ddims[i] = std::max(x_dims[i], y_dims[i]);
-      x_strides[i] = x_dims[i + 1] * x_strides[i + 1];
-      y_strides[i] = y_dims[i + 1] * y_strides[i + 1];
+      if (x_strides_override.empty()) {
+        x_strides[i] = x_dims[i + 1] * x_strides[i + 1];
+      }
+      if (y_strides_override.empty()) {
+        y_strides[i] = y_dims[i + 1] * y_strides[i + 1];
+      }
       out_strides[i] = out_ddims[i + 1] * out_strides[i + 1];
     }
 
@@ -146,9 +238,11 @@ void ExecuteMatMulV2(const ExecutionContext& ctx,
                      const Tensor* y, std::vector<int64_t>& y_dims,
                      bool trans_y, Tensor* out, std::vector<int64_t>& out_dims,
                      int execution_number = 0) {
+  std::vector<int64_t> x_strides_override = GetInputStrides(ctx, "X");
+  std::vector<int64_t> y_strides_override = GetInputStrides(ctx, "Y");
   MatMulV2MKLDNNHandler<T> handler(onednn_engine, ctx.GetPlace(), x_dims,
-                                   trans_x, y_dims, trans_y,
-                                   IsOutputFused(ctx));
+                                   trans_x, y_dims, trans_y, IsOutputFused(ctx),
+                                   x_strides_override, y_strides_override);
 
   const auto src_memory_p = handler.AcquireSrcMemory(x);
   const auto weights_memory_p = handler.AcquireWeightsMemory(y);
@@ -171,6 +265,17 @@ void ExecuteMatMulV2(const ExecutionContext& ctx,
   out->set_format(format);
 }
 
+DDim GetDimForInput(const paddle::framework::ExecutionContext& ctx,
+                    const std::string& input_name) {
+  auto shape = ctx.Attr<std::vector<int>>("fused_reshape_" + input_name);
+  auto axis = ctx.Attr<std::vector<int>>("fused_transpose_" + input_name);
+  auto dim = ctx.Input<paddle::framework::Tensor>(input_name)->dims();
+  if (!shape.empty() && !axis.empty()) {
+    dim = dim.reshape(shape).transpose(axis);
+  }
+  return dim;
+}
+
 template <typename T>
 class MatMulV2MKLDNNKernel : public paddle::framework::OpKernel<T> {
  public:
@@ -230,11 +335,11 @@ class MatMulV2MKLDNNKernel : public paddle::framework::OpKernel<T> {
     bool trans_x = ctx.Attr<bool>("trans_x");
     bool trans_y = ctx.Attr<bool>("trans_y");
 
-    auto x_dims = vectorize(x->dims());
-    auto y_dims = vectorize(y->dims());
+    auto x_dims = vectorize(GetDimForInput(ctx, "X"));
+    auto y_dims = vectorize(GetDimForInput(ctx, "Y"));
     auto out_dims = vectorize(out->dims());
 
-    int ndims = std::max(x->dims().size(), y->dims().size());
+    int ndims = std::max(x_dims.size(), y_dims.size());
     ndims = std::max(ndims, 3);
 
     std::vector<int64_t> x_bd_dims(ndims, 1);
@@ -398,8 +503,6 @@ class MatMulV2GradMKLDNNKernel : public paddle::framework::OpKernel<T> {
 };
 }  // anonymous namespace
 
-namespace ops = paddle::operators;
-
 REGISTER_OP_KERNEL(matmul_v2, MKLDNN, ::paddle::platform::CPUPlace,
                    MatMulV2MKLDNNKernel<float>,
                    MatMulV2MKLDNNKernel<paddle::platform::bfloat16>);

python/paddle/fluid/contrib/slim/quantization/quant2_int8_mkldnn_pass.py

@@ -638,6 +638,8 @@ class Quant2Int8MkldnnPass(object):
         graph = self._apply_pass(graph, 'scale_matmul_fuse_pass')
         graph = self._apply_pass(graph,
                                  'reshape_transpose_matmul_mkldnn_fuse_pass')
+        graph = self._apply_pass(graph,
+                                 'reshape_transpose_matmul_v2_mkldnn_fuse_pass')
         graph = self._apply_pass(
             graph, 'cpu_quantize_pass', ['quant_var_scales', 'data_layout'],
             [self._var_quant_scales, self._get_data_layout(graph)])

python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_reshape_transpose_matmul_v2_fuse_pass.py

+# Copyright (c) 2021 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
+from inference_pass_test import InferencePassTest
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from paddle.fluid.core import AnalysisConfig
+from paddle.fluid.core import PassVersionChecker
+
+
+class TestReshapeTransposeMatmulV2OneDNNFusePass(InferencePassTest):
+    def setUp(self):
+        self.set_params()
+        self.tranpose_perm = [0, 2, 1, 3]
+        self.pass_name = 'reshape_transpose_matmul_v2_mkldnn_fuse_pass'
+
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(
+                name="data", shape=self.data_shape, dtype="float32")
+            weight = fluid.layers.create_parameter(
+                shape=self.weight_shape, dtype="float32")
+            reshape = fluid.layers.reshape(data, shape=self.reshape_shape)
+            transpose = fluid.layers.transpose(reshape, self.tranpose_perm)
+            matmul = paddle.matmul(
+                transpose,
+                weight,
+                transpose_x=self.transpose_x,
+                transpose_y=self.transpose_y)
+
+        self.fetch_list = [matmul]
+        self.enable_mkldnn = True
+
+    def set_params(self):
+        self.data_shape = [-1, 128, 768]
+        self.weight_shape = [1, 12, 64, 128]
+        self.feeds = {"data": np.random.random((1, 128, 768)).astype("float32")}
+        self.transpose_x = False
+        self.transpose_y = False
+        self.reshape_shape = [0, 0, 12, 64]
+
+    def test_check_output(self):
+        use_gpu = False
+        self.check_output_with_option(use_gpu)
+
+    def test_pass_compatible(self):
+        self.assertTrue(PassVersionChecker.IsCompatible(self.pass_name))
+
+
+class TestReshapeTransposeMatmulV2OneDNNFusePassBroadcast(
+        TestReshapeTransposeMatmulV2OneDNNFusePass):
+    def set_params(self):
+        self.data_shape = [2, 64, 16]
+        self.weight_shape = [1, 2, 8, 64]
+        self.feeds = {"data": np.random.random((2, 64, 16)).astype("float32")}
+        self.transpose_x = True
+        self.transpose_y = True
+        self.reshape_shape = [0, 0, 2, 8]
+
+
+if __name__ == "__main__":
+    paddle.enable_static()
+    unittest.main()

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

@@ -252,7 +252,7 @@ class TestDnnlMatMulOpInt8ForceFP32BasicScales(TestDnnlMatMulOp):
 
 
 @skip_check_grad_ci(reason="DNNL's MatMul doesn't implement grad kernel.")
-class TestMatMulOpReshapeTranspose(OpTest):
+class TestReshapeTransposeMatMulOp(OpTest):
     def init_data_type(self):
         self.data_type_ = 'float32'
 
@@ -267,10 +267,12 @@ class TestMatMulOpReshapeTranspose(OpTest):
         self.fused_reshape_Y = []
         self.fused_transpose_Y = []
 
-    def setUp(self):
-        # Set max isa, otherwise fails on SKX and earlier
-        os.environ["DNNL_MAX_CPU_ISA"] = "AVX"
+    def set_op_type_and_transpose_y_name(self):
         self.op_type = "matmul"
+        self.transpose_y_name = "transpose_Y"
+
+    def setUp(self):
+        self.set_op_type_and_transpose_y_name()
         self._cpu_only = True
         self.use_mkldnn = True
         self.transpose_y = True
@@ -280,7 +282,7 @@ class TestMatMulOpReshapeTranspose(OpTest):
         self.inputs = {'X': self.x, 'Y': self.y}
         self.attrs = {
             'use_mkldnn': self.use_mkldnn,
-            'transpose_Y': self.transpose_y
+            self.transpose_y_name: self.transpose_y
         }
         if len(self.fused_transpose_X) > 0:
             self.attrs['fused_transpose_X'] = self.fused_transpose_X
@@ -297,7 +299,7 @@ class TestMatMulOpReshapeTranspose(OpTest):
         self.check_output()
 
 
-class TestMatMulOpReshapeTranspose4DXFloat(TestMatMulOpReshapeTranspose):
+class TestReshapeTransposeMatMulOp4DXFloat(TestReshapeTransposeMatMulOp):
     def generate_data(self):
         self.x = np.random.random([2, 128, 768]).astype("float32")
         self.y = np.random.random([2, 128, 768]).astype("float32").reshape(
@@ -311,12 +313,12 @@ class TestMatMulOpReshapeTranspose4DXFloat(TestMatMulOpReshapeTranspose):
             self.y.transpose([0, 1, 3, 2]))
 
 
-class TestMatMulOpReshapeTranspose4DXInt8(TestMatMulOpReshapeTranspose4DXFloat):
+class TestReshapeTransposeMatMulOp4DXInt8(TestReshapeTransposeMatMulOp4DXFloat):
     def init_data_type(self):
         self.data_type_ = 'int8'
 
 
-class TestMatMulOpReshapeTranspose4DYFloat(TestMatMulOpReshapeTranspose):
+class TestReshapeTransposeMatMulOp4DYFloat(TestReshapeTransposeMatMulOp):
     def generate_data(self):
         self.x = np.random.random([2, 128, 768]).astype("float32").reshape(
             [2, 128, 12, 64]).transpose([0, 2, 1, 3])
@@ -329,12 +331,12 @@ class TestMatMulOpReshapeTranspose4DYFloat(TestMatMulOpReshapeTranspose):
             self.x, self.y.reshape([2, 128, 12, 64]).transpose([0, 2, 3, 1]))
 
 
-class TestMatMulOpReshapeTranspose4DYInt8(TestMatMulOpReshapeTranspose4DYFloat):
+class TestReshapeTransposeMatMulOp4DYInt8(TestReshapeTransposeMatMulOp4DYFloat):
     def init_data_type(self):
         self.data_type_ = 'int8'
 
 
-class TestMatMulOpReshapeTranspose4DXYFloat(TestMatMulOpReshapeTranspose):
+class TestReshapeTransposeMatMulOp4DXYFloat(TestReshapeTransposeMatMulOp):
     def generate_data(self):
         self.x = np.random.random([2, 128, 768]).astype("float32")
         self.y = np.random.random([2, 128, 768]).astype("float32")
@@ -347,13 +349,13 @@ class TestMatMulOpReshapeTranspose4DXYFloat(TestMatMulOpReshapeTranspose):
             self.y.reshape([2, 128, 12, 64]).transpose([0, 2, 3, 1]))
 
 
-class TestMatMulOpReshapeTranspose4DXYInt8(
-        TestMatMulOpReshapeTranspose4DXYFloat):
+class TestReshapeTransposeMatMulOp4DXYInt8(
+        TestReshapeTransposeMatMulOp4DXYFloat):
     def init_data_type(self):
         self.data_type_ = 'int8'
 
 
-class TestMatMulOpReshapeTranspose2DXFloat(TestMatMulOpReshapeTranspose):
+class TestReshapeTransposeMatMulOp2DXFloat(TestReshapeTransposeMatMulOp):
     def generate_data(self):
         self.x = np.random.random([2, 5, 10]).astype("float32")
         self.y = np.random.random([2, 5, 10]).astype("float32").reshape(
@@ -367,12 +369,12 @@ class TestMatMulOpReshapeTranspose2DXFloat(TestMatMulOpReshapeTranspose):
             self.y.transpose([1, 0]))
 
 
-class TestMatMulOpReshapeTranspose2DXInt8(TestMatMulOpReshapeTranspose2DXFloat):
+class TestReshapeTransposeMatMulOp2DXInt8(TestReshapeTransposeMatMulOp2DXFloat):
     def init_data_type(self):
         self.data_type_ = 'int8'
 
 
-class TestMatMulOpReshapeTranspose2DYFloat(TestMatMulOpReshapeTranspose):
+class TestReshapeTransposeMatMulOp2DYFloat(TestReshapeTransposeMatMulOp):
     def generate_data(self):
         self.x = np.random.random([2, 5, 10]).astype("float32").reshape(
             [10, 10]).transpose([1, 0])
@@ -384,12 +386,12 @@ class TestMatMulOpReshapeTranspose2DYFloat(TestMatMulOpReshapeTranspose):
         self.out = np.matmul(self.x, self.y.reshape([10, 10]))
 
 
-class TestMatMulOpReshapeTranspose2DYInt8(TestMatMulOpReshapeTranspose2DYFloat):
+class TestReshapeTransposeMatMulOp2DYInt8(TestReshapeTransposeMatMulOp2DYFloat):
     def init_data_type(self):
         self.data_type_ = 'int8'
 
 
-class TestMatMulOpReshapeTranspose3DXFloat(TestMatMulOpReshapeTranspose):
+class TestReshapeTransposeMatMulOp3DXFloat(TestReshapeTransposeMatMulOp):
     def generate_data(self):
         self.x = np.random.random([2, 2, 5, 5]).astype("float32")
         self.y = np.random.random([2, 2, 5, 5]).astype("float32").reshape(
@@ -403,12 +405,12 @@ class TestMatMulOpReshapeTranspose3DXFloat(TestMatMulOpReshapeTranspose):
             self.y.transpose(0, 2, 1))
 
 
-class TestMatMulOpReshapeTranspose3DXInt8(TestMatMulOpReshapeTranspose3DXFloat):
+class TestReshapeTransposeMatMulOp3DXInt8(TestReshapeTransposeMatMulOp3DXFloat):
     def init_data_type(self):
         self.data_type_ = 'int8'
 
 
-class TestMatMulOpReshapeTranspose3DYFloat(TestMatMulOpReshapeTranspose):
+class TestReshapeTransposeMatMulOp3DYFloat(TestReshapeTransposeMatMulOp):
     def generate_data(self):
         self.x = np.random.random([2, 2, 5, 5]).astype(self.data_type_).reshape(
             [2, 10, 5]).transpose([0, 2, 1])
@@ -420,7 +422,7 @@ class TestMatMulOpReshapeTranspose3DYFloat(TestMatMulOpReshapeTranspose):
         self.out = np.matmul(self.x, self.y.reshape([2, 10, 5]))
 
 
-class TestMatMulOpReshapeTranspose3DYInt8(TestMatMulOpReshapeTranspose3DYFloat):
+class TestReshapeTransposeMatMulOp3DYInt8(TestReshapeTransposeMatMulOp3DYFloat):
     def init_data_type(self):
         self.data_type_ = 'int8'
 

python/paddle/fluid/tests/unittests/mkldnn/test_matmul_v2_mkldnn_op.py

@@ -29,7 +29,11 @@ from paddle.fluid.tests.unittests.mkldnn.test_matmul_mkldnn_op import (
     TestMatMulOpTransposeReshapeOtherDimFloat,
     TestMatMulOpTransposeReshapeTransposeAxisNotSupportedException,
     TestMatMulOpTransposeReshapeTransposeRankNotSupportedException,
-    TestMatMulOpTransposeReshapeRankOfReshapeNotSupportedException)
+    TestMatMulOpTransposeReshapeRankOfReshapeNotSupportedException,
+    TestReshapeTransposeMatMulOp, TestReshapeTransposeMatMulOp4DXFloat,
+    TestReshapeTransposeMatMulOp4DYFloat, TestReshapeTransposeMatMulOp4DXYFloat,
+    TestReshapeTransposeMatMulOp2DXFloat, TestReshapeTransposeMatMulOp2DYFloat,
+    TestReshapeTransposeMatMulOp3DXFloat, TestReshapeTransposeMatMulOp3DYFloat)
 
 
 def reference_matmul(X, Y, transpose_x=False, transpose_y=False):
@@ -434,6 +438,61 @@ class TestMatMulV2OpTransposeReshapeTransposeRankNotSupportedException(
         self.op_type = "matmul_v2"
 
 
+class TestMatMulV2OpReshapeTranspose(TestReshapeTransposeMatMulOp):
+    def set_op_type_and_transpose_y_name(self):
+        self.op_type = "matmul_v2"
+        self.transpose_y_name = "trans_y"
+
+
+class TestMatMulV2OpReshapeTranspose4DXFloat(
+        TestReshapeTransposeMatMulOp4DXFloat):
+    def set_op_type_and_transpose_y_name(self):
+        self.op_type = "matmul_v2"
+        self.transpose_y_name = "trans_y"
+
+
+class TestMatMulV2OpReshapeTranspose4DYFloat(
+        TestReshapeTransposeMatMulOp4DYFloat):
+    def set_op_type_and_transpose_y_name(self):
+        self.op_type = "matmul_v2"
+        self.transpose_y_name = "trans_y"
+
+
+class TestMatMulV2OpReshapeTranspose4DXYFloat(
+        TestReshapeTransposeMatMulOp4DXYFloat):
+    def set_op_type_and_transpose_y_name(self):
+        self.op_type = "matmul_v2"
+        self.transpose_y_name = "trans_y"
+
+
+class TestMatMulV2OpReshapeTranspose2DXFloat(
+        TestReshapeTransposeMatMulOp2DXFloat):
+    def set_op_type_and_transpose_y_name(self):
+        self.op_type = "matmul_v2"
+        self.transpose_y_name = "trans_y"
+
+
+class TestMatMulV2OpReshapeTranspose2DYFloat(
+        TestReshapeTransposeMatMulOp2DYFloat):
+    def set_op_type_and_transpose_y_name(self):
+        self.op_type = "matmul_v2"
+        self.transpose_y_name = "trans_y"
+
+
+class TestMatMulV2OpReshapeTranspose3DXFloat(
+        TestReshapeTransposeMatMulOp3DXFloat):
+    def set_op_type_and_transpose_y_name(self):
+        self.op_type = "matmul_v2"
+        self.transpose_y_name = "trans_y"
+
+
+class TestMatMulV2OpReshapeTranspose3DYFloat(
+        TestReshapeTransposeMatMulOp3DYFloat):
+    def set_op_type_and_transpose_y_name(self):
+        self.op_type = "matmul_v2"
+        self.transpose_y_name = "trans_y"
+
+
 if __name__ == "__main__":
     paddle.enable_static()
     unittest.main()