cuBlasLt Epilogue To Fuse Linear + ReLU|GeLU (#39437)

* Added cuBlasLtHandle_t to device context.

* Added fused_gemm_epilogue op.

1. Added fused_gemm_epilogue op to leverage cuBlastLt Epilogue.
2. Support fusion Act(X*Y + bias), X'dims >=2 and Y'dims shoule be 2.
2. Act currently only be supported ReLU. (Will add GeLU in the future).

* Added UT to fused_gemm_epilogue op.

* Added LinearAct Pattern

1. Added LinearAct into graph_pattern_detector.* to define (2.)'s
pattern.
2. LinearAct is used to detect act(element_add(matmul_v2(x, w), bias)).
3. act currently only support ReLU (Will support GeLU in the future).

* Added FuseGemmEpiloguePass

1, Added FuseGemmEpiloguePass to handle nn.Linear + Act{ReLU}
fusion (GeLU will be supported in the future).
2. Only support matmul_v2 from nn.Linear.

* Added pybind to BuildStrageter.fuse_gemm_epilogue_.

* Added UT for fuse_gemm_epilogue_pass.

* GeLU support and EpilogueSingleton

1. Added GeLU support to fused_gemm_epilogue op.
2. Added EpilogueSingleton to cache auxiliary pointer.
3. Added related UTs.

* Rename cublaslt_epilogue_opto gemm_epilogue_op.*.

* Added both train and infer pattern to LinearAct.

1. Added support of fwd graph with grap_ops linking to LinearAct.
2. Added related changes to fuse_gemm_epilogue_pass for above
modification.

* Changed CUDA requirement from 11.4 to 11.6 for fuse_gemm_epilogue_pass.

* Added identity activation support to gemm_epilogue_op.

* Added Linear Fusion (matmul_v2 + ele_add)

1. Added matmul_v2 + ele_add pattern to LinearActPattern.
2. Added matmul_v2 + ele_add support to fuse_gemm_epilogue_pass.

* Rename gemm_epilogue_op.* to fused_gemm_epilogue_op.*

* Add fused_gemm_epilogue_grad op.

1. Added fused_gemm_epilogue_grad to support backward epilogue fusion.

* Add UTs to fused_gemm_epilogue_grad_op.

* Change attribute name in fused_gemm_epilogue_grad_op for clearing.

* Allow DX and DBias be dispensable to fused_gemm_epilogue_grad op.

* Added ElementwiseAdd+Matmul+Act graph pattern detection.

* Fuse backward of Linear( Act(x))

1. Added backward fusion pass to Linear( Act(x)).
2. Added backward fusion pass to Linear(x).

* Added UTs to backward fusion of Linear(Act(x)).

* Complete document of arguments to fused_gemm_epilogue_op.

* Made arguments of some functions pass by reference.

* Modify code with review comments.

1. Made arguments of some function pass by reference.
2. Removed redundant code.
3. Followed Google code style to change code.

* Made 'const' code style be consistent

* Fixed random seed of python UTs.

* Set Compiling constrains to cuBlasLt

1. Require CUDA 11.6+
2. Remove fuse_gemm_epilogue related tests when CUDA < 11.6.

* Code Reivew from Paddle

1. Changed arguments name is_first_gemm to without_x_gradient for
clearing.
2. Applied PADDLE_THROW in fused_gemm_epilogue_op.

* Remove EpilogueSingleton

1. Applied ReserveSpace to replace Epilogue for passing auxiliary
pointers between FWD and BWD.

* Fix a logical error and enhance UTs.

1. Added act op count checking in UTs.
2. Fix issue to fuse backward or ReLU(Linear(X)).
3. TODO: solve GELU fusion issues.

* Fix Linear and GeLU fusion issues.

1. Modified graph_detech_pattern to fit with both linear wiht gelu or
relu.
2. Modified data range in Uts to allow negative values.

* Removed fused_gemm_epilogue_op.h.

* Rename namespace pten to phi.

* Rename name of arguments in fused_gemm_epilogue_op

1. bias -> Bias.
2. out -> Out.
3. reserve_space -> ReserveSpace.

* Change EpiloguePassActivationCache as local variable.

1. Removed singleton in EpiloguePassActivationCache.
2. Made EpiloguePassActivationCache as an argument to each pass
functions.

paddle/fluid/framework/details/CMakeLists.txt

@@ -139,7 +139,7 @@ set(IR_PASS_DEPS graph_viz_pass multi_devices_graph_pass
     coalesce_grad_tensor_pass fuse_all_reduce_op_pass backward_optimizer_op_deps_pass
     fuse_adam_op_pass fuse_sgd_op_pass fuse_momentum_op_pass
     sync_batch_norm_pass runtime_context_cache_pass graph_to_program_pass
-    fix_op_run_order_pass)
+    fix_op_run_order_pass fuse_gemm_epilogue_pass)
 
 if (WITH_CINN)
   set(IR_PASS_DEPS ${IR_PASS_DEPS} build_cinn_pass)

paddle/fluid/framework/details/build_strategy.cc

 /* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+Copyright (c) 2022 NVIDIA 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.
@@ -175,6 +176,11 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
     !defined(_WIN32) && !defined(__APPLE__)
     AppendPassWithCheck(strategy_.enable_auto_fusion_, "fusion_group_pass");
 #endif
+
+#if (defined(PADDLE_WITH_CUDA) && CUDA_VERSION >= 11060)
+    AppendPassWithCheck(strategy_.fuse_gemm_epilogue_,
+                        "fuse_gemm_epilogue_pass");
+#endif
     AppendPassWithCheck(strategy_.fuse_elewise_add_act_ops_,
                         "fuse_elewise_add_act_pass");
     // for single card training, fuse_all_reduce_ops is unnecessary.
@@ -507,3 +513,6 @@ USE_PASS(mkldnn_placement_pass);
     !defined(_WIN32) && !defined(__APPLE__)
 USE_PASS(fusion_group_pass);
 #endif
+#if (defined(PADDLE_WITH_CUDA) && CUDA_VERSION >= 11060)
+USE_PASS(fuse_gemm_epilogue_pass);
+#endif

paddle/fluid/framework/details/build_strategy.h

 // Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+// Copyright (c) 2022 NVIDIA 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.
@@ -124,6 +125,8 @@ struct BuildStrategy {
   paddle::optional<bool> fuse_broadcast_ops_{paddle::none};
   // replace batch_norm with sync_batch_norm.
   bool sync_batch_norm_{false};
+  // Fuse GEMM+Epilogue via cublasLt epilogue.
+  bool fuse_gemm_epilogue_{false};
 
   // mkldnn_enabled_op_types specify the operator type list to
   // use MKLDNN acceleration. It is null in default, means

paddle/fluid/framework/ir/CMakeLists.txt

@@ -157,6 +157,7 @@ endif()
 cc_library(fuse_bn_act_pass SRCS fuse_bn_act_pass.cc DEPS pass graph_pattern_detector )
 cc_library(fuse_bn_add_act_pass SRCS fuse_bn_add_act_pass.cc DEPS pass graph_pattern_detector )
 cc_library(fuse_elewise_add_act_pass SRCS fuse_elewise_add_act_pass.cc DEPS pass graph_pattern_detector )
+cc_library(fuse_gemm_epilogue_pass SRCS fuse_gemm_epilogue_pass.cc DEPS pass graph_pattern_detector )
 cc_library(fuse_relu_depthwise_conv_pass SRCS fuse_relu_depthwise_conv_pass.cc DEPS pass graph_pattern_detector )
 
 set(GLOB_PASS_LIB ${PASS_LIBRARY} CACHE INTERNAL "Global PASS library")

paddle/fluid/framework/ir/fuse_gemm_epilogue_pass.h

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+// Copyright (c) 2022 NVIDIA 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 <mutex>
+#include <string>
+#include <unordered_set>
+
+#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"
+#include "paddle/fluid/framework/ir/pass.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+/*
+ * Fuse the ElewiseAdd and activation
+ */
+class Graph;
+class Node;
+
+class EpiloguePassActivationCache {
+ public:
+  EpiloguePassActivationCache() {}
+
+  EpiloguePassActivationCache(const EpiloguePassActivationCache &) = delete;
+  void operator=(const EpiloguePassActivationCache &) = delete;
+
+  bool HasFusedActivation(const std::string &key) const {
+    return fused_activation_space_map_.count(key);
+  }
+
+  ir::Node *GetFusedActivationSpace(const std::string &key) {
+    if (HasFusedActivation(key)) {
+      return fused_activation_space_map_.find(key)->second;
+    }
+    PADDLE_THROW(platform::errors::InvalidArgument(
+        "The key (%d) of EpiloguePassActivationCache does not exist.", key));
+  }
+
+  void InsertFusedActivation(const std::string &key, ir::Node *const value) {
+    if (!HasFusedActivation(key)) {
+      mtx.lock();
+      fused_activation_space_map_.insert({key, value});
+      mtx.unlock();
+    } else {
+      PADDLE_THROW(platform::errors::AlreadyExists(
+          "The key (%d) of EpiloguePassActivationCache already exist.", key));
+    }
+  }
+
+ private:
+  std::unordered_map<std::string, ir::Node *> fused_activation_space_map_;
+  std::mutex mtx;
+};
+
+class FuseGemmEpiloguePass : public FusePassBase {
+ public:
+  virtual ~FuseGemmEpiloguePass() {}
+
+ protected:
+  void ApplyImpl(ir::Graph *graph) const override;
+
+  ir::Graph *FuseLinearFwd(ir::Graph *graph, bool is_training) const;
+  ir::Graph *FuseLinearActFwd(ir::Graph *graph,
+                              const std::unordered_set<std::string> &act_types,
+                              bool is_training, bool is_act_grad_x_from_act,
+                              EpiloguePassActivationCache *cache) const;
+  ir::Graph *FuseLinearBwd(ir::Graph *graph, bool without_x_gradient) const;
+  ir::Graph *FuseLinearActBwd(
+      ir::Graph *graph, const std::unordered_set<std::string> &act_grad_types,
+      bool is_act_grad_x_from_act, EpiloguePassActivationCache *cache) const;
+
+ private:
+  bool IsGemmFromLinear_(const std::vector<int64_t> &x_shape,
+                         const std::vector<int64_t> &w_shape,
+                         OpDesc *matmul_v2_op) const;
+  const std::string GetReserveSpaceCacheKey(const std::string var_name,
+                                            int block_id) const {
+    return std::to_string(block_id) + var_name;
+  }
+};
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -1461,31 +1461,6 @@ PDNode *patterns::BatchNormAddActGrad::operator()(
   return bn_grad;
 }
 
-PDNode *patterns::ElewiseAddAct::operator()(
-    paddle::framework::ir::PDNode *ele_x_var,
-    std::unordered_set<std::string> act_types) {
-  auto *ele_y_var = pattern->NewNode(ele_y_repr())
-                        ->assert_is_op_input("elementwise_add", "Y");
-
-  auto *ele_add =
-      pattern->NewNode(ele_add_repr())->assert_is_op("elementwise_add");
-
-  auto *ele_out_var = pattern->NewNode(elewise_add_out_repr())
-                          ->assert_is_op_output("elementwise_add", "Out");
-
-  ele_out_var->AsIntermediate()->assert_is_ops_input(act_types);
-
-  auto *act = pattern->NewNode(act_repr())->assert_is_ops(act_types);
-
-  auto *act_out_var =
-      pattern->NewNode(act_out_repr())->assert_is_ops_output(act_types, "Out");
-
-  ele_add->LinksFrom({ele_x_var, ele_y_var}).LinksTo({ele_out_var});
-  act->LinksFrom({ele_out_var}).LinksTo({act_out_var});
-
-  return act_out_var;
-}
-
 PDNode *patterns::ElewiseAddActInplaceGrad::operator()(
     paddle::framework::ir::PDNode *d_act_out_var,
     std::unordered_set<std::string> act_types) {
@@ -1526,6 +1501,159 @@ PDNode *patterns::ElewiseAddActInplaceGrad::operator()(
   return ele_add_grad;
 }
 
+PDNode *patterns::ElewiseAddAct::operator()(
+    paddle::framework::ir::PDNode *ele_x_var,
+    std::unordered_set<std::string> act_types) {
+  auto *ele_y_var = pattern->NewNode(ele_y_repr())
+                        ->assert_is_op_input("elementwise_add", "Y");
+
+  auto *ele_add =
+      pattern->NewNode(ele_add_repr())->assert_is_op("elementwise_add");
+
+  auto *ele_out_var = pattern->NewNode(elewise_add_out_repr())
+                          ->assert_is_op_output("elementwise_add", "Out");
+
+  ele_out_var->AsIntermediate()->assert_is_ops_input(act_types);
+
+  auto *act = pattern->NewNode(act_repr())->assert_is_ops(act_types);
+
+  auto *act_out_var =
+      pattern->NewNode(act_out_repr())->assert_is_ops_output(act_types, "Out");
+
+  ele_add->LinksFrom({ele_x_var, ele_y_var}).LinksTo({ele_out_var});
+  act->LinksFrom({ele_out_var}).LinksTo({act_out_var});
+
+  return act_out_var;
+}
+
+PDNode *patterns::LinearAct::operator()(
+    paddle::framework::ir::PDNode *linear_x_var,
+    const std::unordered_set<std::string> &act_types, bool with_grad_link,
+    bool is_act_grad_x_from_act) {
+  auto *matmul_w_var =
+      pattern->NewNode(matmul_w_repr())->assert_is_op_input("matmul_v2", "Y");
+
+  auto *matmul = pattern->NewNode(matmul_repr())->assert_is_op("matmul_v2");
+
+  auto *matmul_out_var = pattern->NewNode(matmul_out_repr())
+                             ->assert_is_op_output("matmul_v2", "Out");
+
+  matmul_out_var->AsIntermediate()->assert_is_op_input("elementwise_add", "X");
+
+  auto *ele_bias_var = pattern->NewNode(ele_bias_repr())
+                           ->assert_is_op_input("elementwise_add", "Y");
+
+  auto *ele_add =
+      pattern->NewNode(ele_add_repr())->assert_is_op("elementwise_add");
+
+  auto *ele_out_var = pattern->NewNode(elewise_add_out_repr())
+                          ->assert_is_op_output("elementwise_add", "Out");
+
+  matmul->LinksFrom({linear_x_var, matmul_w_var}).LinksTo({matmul_out_var});
+  ele_add->LinksFrom({matmul_out_var, ele_bias_var}).LinksTo({ele_out_var});
+
+  if (with_grad_link) {
+    matmul_out_var->assert_is_op_input("elementwise_add_grad", "X");
+    auto *elementwise_add_grad_op = pattern->NewNode("elementwise_add_grad")
+                                        ->assert_is_op("elementwise_add_grad");
+    elementwise_add_grad_op->LinksFrom({matmul_out_var});
+  }
+
+  if (act_types.size() > 0) {
+    ele_out_var->AsIntermediate()->assert_is_ops_input(act_types);
+
+    auto *act = pattern->NewNode(act_repr())->assert_is_ops(act_types);
+    auto *act_out_var = pattern->NewNode(act_out_repr())
+                            ->assert_is_ops_output(act_types, "Out");
+
+    act->LinksFrom({ele_out_var}).LinksTo({act_out_var});
+
+    if (with_grad_link && !is_act_grad_x_from_act) {
+      std::unordered_set<std::string> act_grad_types;
+      for (const auto &act : act_types) {
+        std::string act_grad(act);
+        act_grad.append("_grad");
+        act_grad_types.insert(act_grad);
+      }
+
+      ele_out_var->assert_is_ops_input(act_grad_types, "X");
+      auto *act_grad_op =
+          pattern->NewNode(act_grad_repr())->assert_is_ops(act_grad_types);
+      act_grad_op->LinksFrom({ele_out_var});
+    }
+
+    return act_out_var;
+  }
+
+  return ele_out_var;
+}
+
+PDNode *patterns::ElewiseAddMatmulAct::operator()(
+    paddle::framework::ir::PDNode *dout_var,
+    const std::unordered_set<std::string> &act_grad_types,
+    bool without_x_gradient, bool is_act_grad_x_from_act) {
+  auto *ele_grad_bias_var =
+      pattern->NewNode(ele_grad_bias_repr())
+          ->assert_is_op_input("elementwise_add_grad", "Y");
+  auto *ele_add_grad = pattern->NewNode(ele_add_grad_repr())
+                           ->assert_is_op("elementwise_add_grad");
+  auto *ele_grad_dx_var =
+      pattern->NewNode(ele_grad_dx_repr())
+          ->assert_is_op_output("elementwise_add_grad", GradVarName("X"));
+  auto *ele_grad_dbias_var =
+      pattern->NewNode(ele_grad_dbias_repr())
+          ->assert_is_op_output("elementwise_add_grad", GradVarName("Y"));
+  ele_add_grad->LinksFrom({dout_var, ele_grad_bias_var})
+      .LinksTo({ele_grad_dx_var, ele_grad_dbias_var});
+
+  ele_grad_dx_var->AsIntermediate()->assert_is_op_input("matmul_v2_grad",
+                                                        GradVarName("Out"));
+
+  auto *matmul_grad_x_var = pattern->NewNode(matmul_grad_x_repr())
+                                ->assert_is_op_input("matmul_v2_grad", "X");
+  auto *matmul_grad_w_var = pattern->NewNode(matmul_grad_w_repr())
+                                ->assert_is_op_input("matmul_v2_grad", "Y");
+  auto *matmul_grad =
+      pattern->NewNode(matmul_grad_repr())->assert_is_op("matmul_v2_grad");
+  auto *matmul_grad_dx_var =
+      pattern->NewNode(matmul_grad_dx_repr())
+          ->assert_is_op_output("matmul_v2_grad", GradVarName("X"));
+  auto *matmul_grad_dw_var =
+      pattern->NewNode(matmul_grad_dw_repr())
+          ->assert_is_op_output("matmul_v2_grad", GradVarName("Y"));
+  matmul_grad->LinksFrom(
+      {ele_grad_dx_var, matmul_grad_x_var, matmul_grad_w_var});
+  if (without_x_gradient) {
+    matmul_grad->LinksTo({matmul_grad_dw_var});
+  } else {
+    matmul_grad->LinksTo({matmul_grad_dx_var, matmul_grad_dw_var});
+  }
+
+  if (!without_x_gradient && act_grad_types.size() > 0) {
+    matmul_grad_dx_var->AsIntermediate()->assert_is_ops_input(
+        act_grad_types, GradVarName("Out"));
+
+    auto *act_grad =
+        pattern->NewNode(act_grad_repr())->assert_is_ops(act_grad_types);
+    auto *act_grad_dx_var =
+        pattern->NewNode(act_grad_dx_repr())
+            ->assert_is_ops_output(act_grad_types, GradVarName("X"));
+
+    auto *act_grad_x_var = matmul_grad_x_var;
+    if (!is_act_grad_x_from_act) {
+      auto *ele_out_var = pattern->NewNode(ele_out_repr())
+                              ->assert_is_ops_input(act_grad_types, "X");
+      act_grad_x_var = ele_out_var;
+    }
+
+    act_grad->LinksFrom({matmul_grad_dx_var, act_grad_x_var})
+        .LinksTo({act_grad_dx_var});
+    return act_grad;
+  }
+
+  return matmul_grad;
+}
+
 // conv_type: conv2d, conv3d, conv2d_transpose
 PDNode *patterns::ConvBias::operator()(
     paddle::framework::ir::PDNode *conv_input, std::string conv_type) {

paddle/fluid/framework/ir/graph_pattern_detector.h

@@ -863,6 +863,65 @@ struct ElewiseAddActInplaceGrad : public PatternBase {
   PATTERN_DECL_NODE(ele_y);
 };
 
+// The following patterns are used to fuse linear and act (ReLu or GeLU)
+// formula: act(F.linear(x))
+// op: matmul_v2 + elementwise_add + act
+// named nodes: matmul, elementwise_add, act
+//              matmul_w, matmul_out
+//              ele_bias, elewise_add_out, act_out
+struct LinearAct : public PatternBase {
+  LinearAct(PDPattern* pattern, const std::string& name_scope)
+      : PatternBase(pattern, name_scope, "linear_act") {}
+
+  PDNode* operator()(PDNode* x,
+                     const std::unordered_set<std::string>& act_types,
+                     bool with_grad_link, bool is_act_grad_x_from_act);
+
+  // declare operator node's name
+  PATTERN_DECL_NODE(matmul);
+  PATTERN_DECL_NODE(ele_add);
+  PATTERN_DECL_NODE(act);
+  PATTERN_DECL_NODE(act_grad);
+  // declare variable node's name
+  PATTERN_DECL_NODE(matmul_w);
+  PATTERN_DECL_NODE(matmul_out);
+  PATTERN_DECL_NODE(elewise_add_out);
+  PATTERN_DECL_NODE(ele_bias);
+  PATTERN_DECL_NODE(act_out);
+};
+
+// The following patterns are used to fuse linear_grad and act_grad (ReLu or
+// GeLU)
+// formula: the backward of F.linear( act(x) )
+// op: elementwise_add_grad + matmul_v2_grad + act_grad
+// named nodes: ele_add_grad, matmul_grad, act_grad
+//              ele_grad_bias, ele_grad_dx, ele_grad_dbias
+//              matmul_grad_x, matmul_grad_dx, matmul_grad_dx
+//              matmul_grad_dw, act_grad_dx
+struct ElewiseAddMatmulAct : public PatternBase {
+  ElewiseAddMatmulAct(PDPattern* pattern, const std::string& name_scope)
+      : PatternBase(pattern, name_scope, "elewiseadd_matmul_act") {}
+
+  PDNode* operator()(PDNode* x,
+                     const std::unordered_set<std::string>& act_grad_types,
+                     bool without_x_gradient, bool is_act_grad_x_from_act);
+
+  // declare operator node's name
+  PATTERN_DECL_NODE(ele_add_grad);
+  PATTERN_DECL_NODE(matmul_grad);
+  PATTERN_DECL_NODE(act_grad);
+  // declare variable node's name
+  PATTERN_DECL_NODE(ele_out);
+  PATTERN_DECL_NODE(ele_grad_bias);
+  PATTERN_DECL_NODE(ele_grad_dx);
+  PATTERN_DECL_NODE(ele_grad_dbias);
+  PATTERN_DECL_NODE(matmul_grad_x);
+  PATTERN_DECL_NODE(matmul_grad_w);
+  PATTERN_DECL_NODE(matmul_grad_dx);
+  PATTERN_DECL_NODE(matmul_grad_dw);
+  PATTERN_DECL_NODE(act_grad_dx);
+};
+
 // Conv with Elementwise_add as bias
 // op: conv + elementwise_add
 // named nodes:

paddle/fluid/operators/fused/CMakeLists.txt

@@ -19,7 +19,8 @@ register_operators(EXCLUDES
     fused_attention_op
     fused_transformer_op
     fused_feedforward_op
-    resnet_unit_op)
+    resnet_unit_op
+    fused_gemm_epilogue_op)
 
 # fusion_gru_op does not have CUDA kernel
 op_library(fusion_gru_op)
@@ -79,4 +80,8 @@ if (WITH_GPU OR WITH_ROCM)
         cc_test(test_cudnn_norm_conv SRCS cudnn_norm_conv_test.cc DEPS conv_op blas im2col vol2col depthwise_conv eigen_function tensor op_registry device_context generator memory)
         cc_test(test_cudnn_bn_add_relu SRCS cudnn_bn_add_relu_test.cc DEPS batch_norm_op fused_bn_add_activation_op tensor op_registry device_context generator memory)
     endif()
+
+    if (CUDA_VERSION GREATER_EQUAL 11.6)
+        op_library(fused_gemm_epilogue_op)
+    endif()
 endif()

paddle/fluid/operators/fused/fused_gemm_epilogue_op.cc

+/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+Copyright (c) 2022 NVIDIA 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/op_registry.h"
+#include "paddle/fluid/framework/op_version_registry.h"
+
+namespace paddle {
+namespace operators {
+using Tensor = framework::Tensor;
+
+class FusedGemmEpilogueOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "FusedGemmEpilogueOp");
+    OP_INOUT_CHECK(ctx->HasInput("Y"), "Input", "Y", "FusedGemmEpilogueOp");
+    OP_INOUT_CHECK(ctx->HasInput("Bias"), "Output", "Bias",
+                   "FusedGemmEpilogueOp");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out",
+                   "FusedGemmEpilogueOp");
+
+    auto x_dims = ctx->GetInputDim("X");
+    auto y_dims = ctx->GetInputDim("Y");
+    auto bias_dims = ctx->GetInputDim("Bias");
+
+    auto trans_x = ctx->Attrs().Get<bool>("trans_x");
+    auto trans_y = ctx->Attrs().Get<bool>("trans_y");
+
+    PADDLE_ENFORCE_EQ(
+        y_dims.size(), 2,
+        platform::errors::InvalidArgument(
+            "The Input tensor Y's dimension of FusedGemmEpilogueOp "
+            " should be 2, but got %d.",
+            y_dims.size()));
+
+    PADDLE_ENFORCE_GE(
+        x_dims.size(), 2,
+        platform::errors::InvalidArgument(
+            "The Input tensor X's dimension of FusedGemmEpilogueOp "
+            " should be >= 2, but got %d.",
+            x_dims.size()));
+
+    PADDLE_ENFORCE_EQ(
+        bias_dims.size(), 1,
+        platform::errors::InvalidArgument(
+            "The Input tensor bias's dimension of FusedGemmEpilogueOp "
+            " should be == 1, but got %d.",
+            bias_dims.size()));
+
+    PADDLE_ENFORCE_EQ(bias_dims[0], trans_y ? y_dims[0] : y_dims[1],
+                      platform::errors::InvalidArgument(
+                          "The Input tensor bias's dimension 0"
+                          " should be == Y[-1], but got bias's shape = [%s] "
+                          "and Y's shape = [%s]",
+                          bias_dims, y_dims));
+
+    auto x_mat_dims =
+        phi::flatten_to_2d(x_dims, trans_x ? 1 : x_dims.size() - 1);
+
+    int K_from_x = trans_x ? x_mat_dims[0] : x_mat_dims[1];
+    int K_from_y = trans_y ? y_dims[1] : y_dims[0];
+
+    PADDLE_ENFORCE_EQ(
+        K_from_x, K_from_y,
+        platform::errors::InvalidArgument(
+            "The last dimension of X should be equal with Y's first dimension."
+            "But received X[-1] = [%d], Y[0] = [%d].",
+            K_from_x, K_from_y));
+
+    auto activation = ctx->Attrs().Get<std::string>("activation");
+
+    if ((activation != "relu") && (activation != "gelu") &&
+        (activation != "none")) {
+      PADDLE_ENFORCE_EQ(
+          true, false,
+          platform::errors::InvalidArgument(
+              "The activation attribute of fused_gemm_epilogue op should be"
+              " one of {\"none\", \"relu\", \"gelu\"}. But received %s."
+              "But received activation=%s.",
+              activation));
+    }
+
+    if (activation == "none" && ctx->HasOutput("ReserveSpace")) {
+      PADDLE_THROW(platform::errors::InvalidArgument(
+          "The ReserveSpace would not be used when activation = \"none\""));
+    }
+
+    // cublasLt's restriction for auxiliary.
+    if (ctx->HasOutput("ReserveSpace") && activation != "none") {
+      int min_size_of_n = activation == "relu" ? 128 : 8;
+      int N_size = trans_y ? y_dims[0] : y_dims[1];
+      PADDLE_ENFORCE_EQ(N_size % min_size_of_n, 0,
+                        platform::errors::InvalidArgument(
+                            "The output dimension N (X(MxK) * Y(KxN) = C(MxN)) "
+                            "should be multiple of %d when auxiliary_key given "
+                            "and activation=%s, but got N = %d.",
+                            min_size_of_n, activation, N_size));
+    }
+
+    std::vector<int64_t> out_dims;
+    out_dims.reserve(static_cast<size_t>(x_dims.size()));
+    if (trans_x) {
+      for (int i = 1; i < x_dims.size(); ++i) out_dims.push_back(x_dims[i]);
+    } else {
+      for (int i = 0; i < x_dims.size() - 1; ++i) out_dims.push_back(x_dims[i]);
+    }
+
+    if (trans_y) {
+      out_dims.push_back(y_dims[0]);
+    } else {
+      out_dims.push_back(y_dims[1]);
+    }
+
+    ctx->SetOutputDim("Out", phi::make_ddim(out_dims));
+    // Note (Ming Huang): Reserve space of relu is a bit-mask,
+    // which cannot pass nan_and_inf checking if shape is set.
+    if (activation == "gelu" && ctx->HasOutput("ReserveSpace")) {
+      ctx->SetOutputDim("ReserveSpace", phi::make_ddim(out_dims));
+    }
+  }
+
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const {
+    framework::LibraryType library = framework::LibraryType::kPlain;
+    framework::DataLayout layout = framework::DataLayout::kAnyLayout;
+    auto data_type = OperatorWithKernel::IndicateVarDataType(ctx, "X");
+    return framework::OpKernelType(data_type, ctx.GetPlace(), layout, library);
+  }
+};
+
+class FusedGemmEpilogueOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X", "The input tensor X of Out = Act((X * Y) + Bias).");
+    AddInput("Y", "The input tensor Y of Out = Act((X * Y) + Bias).");
+    AddInput("Bias", "The input tensor bias of Out = Act((X * Y) + Bias).");
+
+    AddOutput("Out", "The output tensor Out of Out = Act((X * Y) + Bias).");
+    AddOutput("ReserveSpace",
+              R"DOC(Reserve GPU space to place 
+        auxiliary data pointer. It is used to pass auxiliary data pointer 
+        for fused_gemm_epilogue op. If not given (empty string), the 
+        auxiliary mode would not be enable.)DOC")
+        .AsDispensable()
+        .AsExtra();
+
+    AddAttr<bool>(
+        "trans_x",
+        R"DOC((bool, default false), Whether to transpose input tensor X 
+    or not. The input tensor X coulbe be more than two dimension. When 
+    set trans_x=true, it would fully reverse X. For instant: X with shpae 
+    [d0, d1, d2, d3] -> [d3, d2, d1, d0].)DOC")
+        .SetDefault(false);
+    AddAttr<bool>(
+        "trans_y",
+        R"DOC((bool, default false), Whether to transpose input tensor Y 
+    or not. The input tensor Y should be two dimension. When 
+    set trans_y=true, it would transpose Y. For instant: Y with shpae 
+    [d0, d1] -> [d1, d0].)DOC")
+        .SetDefault(false);
+
+    AddAttr<std::string>(
+        "activation",
+        R"DOC((string, default none), The activation function. It could be 
+    one of {none, relu, gelu}. When none is given, Act would be null 
+    operations)DOC")
+        .SetDefault("none");
+
+    AddComment(R"DOC(
+FusedGemmEpilogue Operator
+This operator is used to perform Activeation(Elementwise_add(Matmul(X, Y), bias)).
+It is equal to paddle.nn.Linear + Activation (None, ReLU or GeLU).
+
+Note:
+X could be more than two dimension and would be flatten to 2D for computing.
+X with shape [d0, d1, d2, d3] -> X_2D with shape [d0*d1*d2, d3]
+)DOC");
+  }
+};
+
+class FusedGemmEpilogueGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    OP_INOUT_CHECK(ctx->HasInput("DOut"), "Input", "DOut",
+                   "FusedGemmEpilogueGradOp");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "FusedGemmEpilogueGradOp");
+    OP_INOUT_CHECK(ctx->HasInput("Y"), "Input", "Y", "FusedGemmEpilogueGradOp");
+    OP_INOUT_CHECK(ctx->HasOutput("DY"), "Output", "DY", "FusedGemmEpilogueOp");
+
+    auto dout_dims = ctx->GetInputDim("DOut");
+    auto x_dims = ctx->GetInputDim("X");
+    auto y_dims = ctx->GetInputDim("Y");
+
+    PADDLE_ENFORCE_GE(
+        dout_dims.size(), 2,
+        platform::errors::InvalidArgument(
+            "The Input tensor DOut's dimension of FusedGemmEpilogueGradOp "
+            " should be >= 2, but got %d.",
+            dout_dims.size()));
+
+    PADDLE_ENFORCE_EQ(
+        y_dims.size(), 2,
+        platform::errors::InvalidArgument(
+            "The Input tensor Y's dimension of FusedGemmEpilogueGradOp "
+            " should be 2, but got %d.",
+            y_dims.size()));
+
+    PADDLE_ENFORCE_GE(
+        x_dims.size(), 2,
+        platform::errors::InvalidArgument(
+            "The Input tensor X's dimension of FusedGemmEpilogueGradOp "
+            " should be >= 2, but got %d.",
+            x_dims.size()));
+
+    PADDLE_ENFORCE_EQ(
+        dout_dims.size(), x_dims.size(),
+        platform::errors::InvalidArgument(
+            "The Input tensor DOut's and X's dimension of "
+            "FusedGemmEpilogueGradOp "
+            " should be the same, but got DOut's dim = %d and X's = %d.",
+            dout_dims.size(), x_dims.size()));
+
+    auto dout_mat_dims = phi::flatten_to_2d(dout_dims, dout_dims.size() - 1);
+
+    auto x_mat_dims = phi::flatten_to_2d(x_dims, x_dims.size() - 1);
+
+    PADDLE_ENFORCE_EQ(
+        dout_mat_dims[1], y_dims[1],
+        platform::errors::InvalidArgument(
+            "The last dimension of DOut should be equal with Y's last"
+            "dimension. But received DOut[-1] = [%d], Y[1] = [%d].",
+            dout_mat_dims[1], y_dims[1]));
+
+    PADDLE_ENFORCE_EQ(
+        dout_mat_dims[0], x_mat_dims[0],
+        platform::errors::InvalidArgument(
+            "The first dimension of DOut should be equal with X's first"
+            "dimension. But received DOut[0] = [%d], Y[0] = [%d].",
+            dout_mat_dims[0], x_mat_dims[0]));
+
+    auto activation_grad = ctx->Attrs().Get<std::string>("activation_grad");
+    if ((activation_grad != "relu_grad") && (activation_grad != "gelu_grad") &&
+        (activation_grad != "none")) {
+      PADDLE_ENFORCE_EQ(
+          true, false,
+          platform::errors::InvalidArgument(
+              "The activation attribute of fused_gemm_epilogue op should be"
+              " one of {\"none\", \"relu\", \"gelu\"}. But received %s."
+              "But received activation=%s.",
+              activation_grad));
+    }
+
+    if (activation_grad != "none" && !ctx->HasInput("ReserveSpace")) {
+      PADDLE_ENFORCE_EQ(true, false,
+                        platform::errors::InvalidArgument(
+                            "The ReserveSpace should not be empty. "
+                            "when activation_grad == {relu_grad, gelu_grad}."));
+    }
+
+    if (ctx->HasOutput("DX")) {
+      std::vector<int64_t> dx_dims;
+      dx_dims.reserve(static_cast<size_t>(x_dims.size()));
+      for (int i = 0; i < x_dims.size(); ++i) {
+        dx_dims.push_back(x_dims[i]);
+      }
+      ctx->SetOutputDim("DX", phi::make_ddim(dx_dims));
+    }
+
+    std::vector<int64_t> dy_dims(y_dims.Get(), y_dims.Get() + y_dims.size());
+    ctx->SetOutputDim("DY", phi::make_ddim(dy_dims));
+
+    if (ctx->HasOutput("DBias")) {
+      std::vector<int64_t> dbias_dims;
+      dbias_dims.push_back(y_dims[1]);
+      ctx->SetOutputDim("DBias", phi::make_ddim(dbias_dims));
+    }
+  }
+
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const {
+    framework::LibraryType library = framework::LibraryType::kPlain;
+    framework::DataLayout layout = framework::DataLayout::kAnyLayout;
+    auto data_type = OperatorWithKernel::IndicateVarDataType(ctx, "DOut");
+    return framework::OpKernelType(data_type, ctx.GetPlace(), layout, library);
+  }
+};
+
+class FusedGemmEpilogueGradOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("DOut",
+             "The input grad tensor to Out of Out = (Act(X) * Y) + bias");
+    AddInput("X", "The input tensor X of Out = (Act(X) * Y) + bias");
+    AddInput("Y", "The input tensor Y of Out = (Act(X) * Y) + bias");
+    AddInput("ReserveSpace",
+             R"DOC(A GPU space to fetch 
+        auxiliary data pointer. It is used to pass auxiliary data pointer 
+        for fused_gemm_epilogue_grad op. If not given (empty string), the 
+        auxiliary mode would not be enable.)DOC")
+        .AsDispensable();
+
+    AddOutput("DX", "The output grad tensor to X of Out = (Act(X) * Y) + bias.")
+        .AsDispensable();
+    AddOutput("DY",
+              "The output grad tensor to Y of Out = (Act(X) * Y) + bias.");
+    AddOutput("DBias",
+              "The output grad tensor to bias of Out = (Act(X) * Y) + bias.")
+        .AsDispensable();
+
+    AddAttr<std::string>(
+        "activation_grad",
+        R"DOC((string, default none), The backward activation function. It could be 
+    one of {none, relu_grad, gelu_grad}. When none is given, The backward Act would 
+    be null operations)DOC")
+        .SetDefault("none");
+
+    AddComment(R"DOC(
+FusedGemmEpilogueGrad Operator
+This operator is used to perform backward of Elementwise_add(Matmul(Activeation(X), Y), bias).
+It is equal to Activation (None, ReLU or GeLU) + paddle.nn.Linear.
+
+Note:
+X could be more than two dimension and would be flatten to 2D for computing.
+X with shape [d0, d1, d2, d3] -> X_2D with shape [d0*d1*d2, d3]
+)DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(fused_gemm_epilogue, ops::FusedGemmEpilogueOp,
+                  ops::FusedGemmEpilogueOpMaker)
+REGISTER_OPERATOR(fused_gemm_epilogue_grad, ops::FusedGemmEpilogueGradOp,
+                  ops::FusedGemmEpilogueGradOpMaker)

paddle/fluid/operators/fused/fused_gemm_epilogue_op.cu

+/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+Copyright (c) 2022 NVIDIA 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/op_registry.h"
+#include "paddle/fluid/framework/op_version_registry.h"
+#include "paddle/fluid/platform/dynload/cublasLt.h"
+#include "paddle/fluid/platform/float16.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename DeviceContext, typename T>
+class FusedGemmEpilogueKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
+
+    const Tensor* x = ctx.Input<Tensor>("X");
+    const Tensor* y = ctx.Input<Tensor>("Y");
+    const Tensor* bias = ctx.Input<Tensor>("Bias");
+
+    Tensor* out = ctx.Output<Tensor>("Out");
+    Tensor* reserve_space = ctx.Output<Tensor>("ReserveSpace");
+
+    bool trans_x = ctx.Attr<bool>("trans_x");
+    bool trans_y = ctx.Attr<bool>("trans_y");
+
+    std::string activation = ctx.Attr<std::string>("activation");
+    bool enable_auxiliary = reserve_space == nullptr ? false : true;
+
+    out->mutable_data<T>(ctx.GetPlace());
+    auto* out_data = out->data<T>();
+
+    auto x_mat_dims =
+        phi::flatten_to_2d(x->dims(), trans_x ? 1 : x->dims().size() - 1);
+    int64_t M = trans_x ? x_mat_dims[1] : x_mat_dims[0];
+    int64_t K = trans_y ? y->dims()[1] : y->dims()[0];
+    int64_t N = trans_y ? y->dims()[0] : y->dims()[1];
+
+    cudaDataType_t mat_type = CUDA_R_32F;
+    cudaDataType_t scale_type = CUDA_R_32F;
+    cublasComputeType_t compute_type = CUBLAS_COMPUTE_32F;
+    if (std::is_same<T, paddle::platform::float16>::value) {
+      mat_type = CUDA_R_16F;
+      scale_type = CUDA_R_16F;
+    }
+    if (std::is_same<T, double>::value) {
+      mat_type = CUDA_R_64F;
+      scale_type = CUDA_R_64F;
+      compute_type = CUBLAS_COMPUTE_64F;
+    }
+
+    cublasLtMatmulDesc_t operation_desc = NULL;
+    PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cublasLtMatmulDescCreate(
+        &operation_desc, compute_type, scale_type));
+    cublasOperation_t transx = trans_x ? CUBLAS_OP_T : CUBLAS_OP_N;
+    cublasOperation_t transy = trans_y ? CUBLAS_OP_T : CUBLAS_OP_N;
+    PADDLE_ENFORCE_GPU_SUCCESS(
+        platform::dynload::cublasLtMatmulDescSetAttribute(
+            operation_desc, CUBLASLT_MATMUL_DESC_TRANSB, &transx,
+            sizeof(transx)));
+    PADDLE_ENFORCE_GPU_SUCCESS(
+        platform::dynload::cublasLtMatmulDescSetAttribute(
+            operation_desc, CUBLASLT_MATMUL_DESC_TRANSA, &transy,
+            sizeof(transy)));
+
+    cublasLtEpilogue_t epiloque_func =
+        get_epilogue_type_(activation, enable_auxiliary);
+    PADDLE_ENFORCE_GPU_SUCCESS(
+        platform::dynload::cublasLtMatmulDescSetAttribute(
+            operation_desc, CUBLASLT_MATMUL_DESC_EPILOGUE, &epiloque_func,
+            sizeof(epiloque_func)));
+    const T* bias_data = bias->data<T>();
+    PADDLE_ENFORCE_GPU_SUCCESS(
+        platform::dynload::cublasLtMatmulDescSetAttribute(
+            operation_desc, CUBLASLT_MATMUL_DESC_BIAS_POINTER, &bias_data,
+            sizeof(bias_data)));
+
+    if (enable_auxiliary && activation != "none") {
+      size_t reserve_space_size = 0;
+      if (activation == "relu") {
+        // Count in bits.
+        reserve_space_size = phi::product(out->dims()) / 8;
+      } else {
+        reserve_space_size = phi::product(out->dims()) * sizeof(T);
+      }
+      reserve_space->mutable_data(ctx.GetPlace(), out->type(),
+                                  reserve_space_size);
+      void* aux_data = reinterpret_cast<void*>(reserve_space->data<T>());
+
+      PADDLE_ENFORCE_GPU_SUCCESS(
+          platform::dynload::cublasLtMatmulDescSetAttribute(
+              operation_desc, CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER,
+              &aux_data, sizeof(aux_data)));
+      PADDLE_ENFORCE_GPU_SUCCESS(
+          platform::dynload::cublasLtMatmulDescSetAttribute(
+              operation_desc, CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_LD, &N,
+              sizeof(N)));
+    }
+
+    cublasLtMatrixLayout_t x_desc = NULL, y_desc = NULL, out_desc = NULL;
+    if (trans_x)
+      PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cublasLtMatrixLayoutCreate(
+          &x_desc, mat_type, M, K, M));
+    else
+      PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cublasLtMatrixLayoutCreate(
+          &x_desc, mat_type, K, M, K));
+    if (trans_y)
+      PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cublasLtMatrixLayoutCreate(
+          &y_desc, mat_type, K, N, K));
+    else
+      PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cublasLtMatrixLayoutCreate(
+          &y_desc, mat_type, N, K, N));
+    PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cublasLtMatrixLayoutCreate(
+        &out_desc, mat_type, N, M, N));
+
+    cublasLtHandle_t lt_handle = dev_ctx.cublaslt_handle();
+    size_t workspace_size = 4 * 1024 * 1024;
+    const cublasLtMatmulAlgo_t* algo = nullptr;
+    cudaStream_t stream = dev_ctx.stream();
+    memory::allocation::AllocationPtr workspace =
+        memory::Alloc(dev_ctx, workspace_size);
+
+    double alpha64 = 1.0, beta64 = 0.0;
+    float alpha32 = 1.0f, beta32 = 0.0f;
+    void *alpha = nullptr, *beta = nullptr;
+    if (std::is_same<T, double>::value) {
+      alpha = &alpha64;
+      beta = &beta64;
+    } else {
+      alpha = &alpha32;
+      beta = &beta32;
+    }
+
+    PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cublasLtMatmul(
+        lt_handle, operation_desc, alpha, y->data<T>(), y_desc, x->data<T>(),
+        x_desc, beta, out_data, out_desc, out_data, out_desc, algo,
+        workspace->ptr(), workspace_size, stream));
+  }
+
+ private:
+  static cublasLtEpilogue_t get_epilogue_type_(const std::string& activation,
+                                               bool enable_auxiliary) {
+    if (activation == "relu") {
+      return enable_auxiliary ? CUBLASLT_EPILOGUE_RELU_AUX_BIAS
+                              : CUBLASLT_EPILOGUE_RELU_BIAS;
+    } else if (activation == "gelu") {
+      return enable_auxiliary ? CUBLASLT_EPILOGUE_GELU_AUX_BIAS
+                              : CUBLASLT_EPILOGUE_GELU_BIAS;
+    } else if (activation == "none") {
+      return CUBLASLT_EPILOGUE_BIAS;
+    } else {
+      PADDLE_ENFORCE_EQ(
+          true, false,
+          platform::errors::InvalidArgument(
+              "The activation attribute of fused_gemm_epilogue op should be"
+              " one of {\"none\", \"relu\", \"gelu\"}. But received %s."
+              "But received activation=%s.",
+              activation));
+    }
+  }
+};
+
+template <typename DeviceContext, typename T>
+class FusedGemmEpilogueGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
+
+    const Tensor* dout = ctx.Input<Tensor>("DOut");
+    const Tensor* x = ctx.Input<Tensor>("X");
+    const Tensor* y = ctx.Input<Tensor>("Y");
+    const Tensor* reserve_space = ctx.Input<Tensor>("ReserveSpace");
+
+    Tensor* dx = ctx.Output<Tensor>("DX");
+    Tensor* dy = ctx.Output<Tensor>("DY");
+    Tensor* dbias = ctx.Output<Tensor>("DBias");
+
+    std::string activation_grad = ctx.Attr<std::string>("activation_grad");
+
+    auto dout_mat_dims =
+        phi::flatten_to_2d(dout->dims(), dout->dims().size() - 1);
+    auto x_mat_dims = phi::flatten_to_2d(x->dims(), x->dims().size() - 1);
+
+    int64_t M = x_mat_dims[0];
+    int64_t K = y->dims()[0];
+    int64_t N = y->dims()[1];
+
+    cudaDataType_t mat_type = CUDA_R_32F;
+    cudaDataType_t scale_type = CUDA_R_32F;
+    cublasComputeType_t compute_type = CUBLAS_COMPUTE_32F;
+    if (std::is_same<T, paddle::platform::float16>::value) {
+      mat_type = CUDA_R_16F;
+      scale_type = CUDA_R_16F;
+    }
+    if (std::is_same<T, double>::value) {
+      mat_type = CUDA_R_64F;
+      scale_type = CUDA_R_64F;
+      compute_type = CUBLAS_COMPUTE_64F;
+    }
+
+    cublasLtHandle_t lt_handle = dev_ctx.cublaslt_handle();
+    size_t workspace_size = 4 * 1024 * 1024;
+    const cublasLtMatmulAlgo_t* algo = nullptr;
+    cudaStream_t stream = dev_ctx.stream();
+
+    double alpha64 = 1.0, beta64 = 0.0;
+    float alpha32 = 1.0f, beta32 = 0.0f;
+    void *alpha = nullptr, *beta = nullptr;
+    if (std::is_same<T, double>::value) {
+      alpha = &alpha64;
+      beta = &beta64;
+    } else {
+      alpha = &alpha32;
+      beta = &beta32;
+    }
+
+    cublasOperation_t trans_dout = CUBLAS_OP_N;
+    cublasLtMatrixLayout_t dout_desc = NULL;
+    PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cublasLtMatrixLayoutCreate(
+        &dout_desc, mat_type, N, M, N));
+
+    if (dx) {
+      cublasLtMatmulDesc_t dx_operation_desc = NULL;
+      PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cublasLtMatmulDescCreate(
+          &dx_operation_desc, compute_type, scale_type));
+      cublasOperation_t trans_y = CUBLAS_OP_T;
+      PADDLE_ENFORCE_GPU_SUCCESS(
+          platform::dynload::cublasLtMatmulDescSetAttribute(
+              dx_operation_desc, CUBLASLT_MATMUL_DESC_TRANSB, &trans_dout,
+              sizeof(trans_dout)));
+      PADDLE_ENFORCE_GPU_SUCCESS(
+          platform::dynload::cublasLtMatmulDescSetAttribute(
+              dx_operation_desc, CUBLASLT_MATMUL_DESC_TRANSA, &trans_y,
+              sizeof(trans_y)));
+      cublasLtEpilogue_t epiloque_func_for_dx =
+          get_epilogue_type_(activation_grad);
+      PADDLE_ENFORCE_GPU_SUCCESS(
+          platform::dynload::cublasLtMatmulDescSetAttribute(
+              dx_operation_desc, CUBLASLT_MATMUL_DESC_EPILOGUE,
+              &epiloque_func_for_dx, sizeof(epiloque_func_for_dx)));
+
+      if (activation_grad != "none") {
+        auto* aux_data = reserve_space->data<T>();
+        PADDLE_ENFORCE_GPU_SUCCESS(
+            platform::dynload::cublasLtMatmulDescSetAttribute(
+                dx_operation_desc, CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER,
+                &aux_data, sizeof(aux_data)));
+        PADDLE_ENFORCE_GPU_SUCCESS(
+            platform::dynload::cublasLtMatmulDescSetAttribute(
+                dx_operation_desc, CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_LD, &N,
+                sizeof(N)));
+      }
+
+      cublasLtMatrixLayout_t y_desc = NULL, dx_desc = NULL;
+      PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cublasLtMatrixLayoutCreate(
+          &y_desc, mat_type, N, K, N));
+      PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cublasLtMatrixLayoutCreate(
+          &dx_desc, mat_type, K, M, K));
+
+      memory::allocation::AllocationPtr dx_workspace =
+          memory::Alloc(dev_ctx, workspace_size);
+
+      dx->mutable_data<T>(ctx.GetPlace());
+      auto* dx_data = dx->data<T>();
+      PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cublasLtMatmul(
+          lt_handle, dx_operation_desc, alpha, y->data<T>(), y_desc,
+          dout->data<T>(), dout_desc, beta, dx_data, dx_desc, dx_data, dx_desc,
+          algo, dx_workspace->ptr(), workspace_size, stream));
+    }
+
+    if (dy) {
+      cublasLtMatmulDesc_t dy_operation_desc = NULL;
+      PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cublasLtMatmulDescCreate(
+          &dy_operation_desc, compute_type, scale_type));
+      cublasOperation_t trans_x = CUBLAS_OP_T;
+      PADDLE_ENFORCE_GPU_SUCCESS(
+          platform::dynload::cublasLtMatmulDescSetAttribute(
+              dy_operation_desc, CUBLASLT_MATMUL_DESC_TRANSA, &trans_dout,
+              sizeof(trans_dout)));
+      PADDLE_ENFORCE_GPU_SUCCESS(
+          platform::dynload::cublasLtMatmulDescSetAttribute(
+              dy_operation_desc, CUBLASLT_MATMUL_DESC_TRANSB, &trans_x,
+              sizeof(trans_x)));
+      cublasLtEpilogue_t epiloque_func_for_dy = dbias == nullptr
+                                                    ? CUBLASLT_EPILOGUE_DEFAULT
+                                                    : CUBLASLT_EPILOGUE_BGRADA;
+      PADDLE_ENFORCE_GPU_SUCCESS(
+          platform::dynload::cublasLtMatmulDescSetAttribute(
+              dy_operation_desc, CUBLASLT_MATMUL_DESC_EPILOGUE,
+              &epiloque_func_for_dy, sizeof(epiloque_func_for_dy)));
+
+      if (dbias) {
+        dbias->mutable_data<T>(ctx.GetPlace());
+        auto* dbias_data = dbias->data<T>();
+        PADDLE_ENFORCE_GPU_SUCCESS(
+            platform::dynload::cublasLtMatmulDescSetAttribute(
+                dy_operation_desc, CUBLASLT_MATMUL_DESC_BIAS_POINTER,
+                &dbias_data, sizeof(dbias_data)));
+      }
+
+      cublasLtMatrixLayout_t x_desc = NULL, dy_desc = NULL;
+      PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cublasLtMatrixLayoutCreate(
+          &x_desc, mat_type, K, M, K));
+      PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cublasLtMatrixLayoutCreate(
+          &dy_desc, mat_type, N, K, N));
+
+      memory::allocation::AllocationPtr dy_workspace =
+          memory::Alloc(dev_ctx, workspace_size);
+
+      dy->mutable_data<T>(ctx.GetPlace());
+      auto* dy_data = dy->data<T>();
+      PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cublasLtMatmul(
+          lt_handle, dy_operation_desc, alpha, dout->data<T>(), dout_desc,
+          x->data<T>(), x_desc, beta, dy_data, dy_desc, dy_data, dy_desc, algo,
+          dy_workspace->ptr(), workspace_size, stream));
+    }
+  }
+
+ private:
+  static cublasLtEpilogue_t get_epilogue_type_(
+      const std::string& activation_grad) {
+    if (activation_grad == "relu_grad") {
+      return CUBLASLT_EPILOGUE_DRELU;
+    } else if (activation_grad == "gelu_grad") {
+      return CUBLASLT_EPILOGUE_DGELU;
+    } else if (activation_grad == "none") {
+      return CUBLASLT_EPILOGUE_DEFAULT;
+    } else {
+      PADDLE_ENFORCE_EQ(
+          true, false,
+          platform::errors::InvalidArgument(
+              "The activation_grad attribute of fused_gemm_epilogue op should "
+              "be"
+              " one of {\"none\", \"relu\", \"gelu\"}. But received %s."
+              "But received activation_grad=%s.",
+              activation_grad));
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+#if CUDA_VERSION >= 11060
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(
+    fused_gemm_epilogue,
+    ops::FusedGemmEpilogueKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::FusedGemmEpilogueKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::FusedGemmEpilogueKernel<paddle::platform::CUDADeviceContext,
+                                 paddle::platform::float16>);
+
+REGISTER_OP_CUDA_KERNEL(
+    fused_gemm_epilogue_grad,
+    ops::FusedGemmEpilogueGradKernel<paddle::platform::CUDADeviceContext,
+                                     float>,
+    ops::FusedGemmEpilogueGradKernel<paddle::platform::CUDADeviceContext,
+                                     double>,
+    ops::FusedGemmEpilogueGradKernel<paddle::platform::CUDADeviceContext,
+                                     paddle::platform::float16>);
+#endif

paddle/fluid/platform/device/gpu/cuda/cuda_helper.h

@@ -19,6 +19,7 @@
 
 #include "paddle/fluid/platform/device/gpu/gpu_types.h"
 #include "paddle/fluid/platform/dynload/cublas.h"
+#include "paddle/fluid/platform/dynload/cublasLt.h"
 #include "paddle/fluid/platform/enforce.h"
 #include "paddle/fluid/platform/macros.h"
 
@@ -110,5 +111,28 @@ class CublasHandleHolder {
   mutable std::mutex mtx_;
 };
 
+class CublasLtHandleHolder {
+ public:
+  CublasLtHandleHolder() {
+    PADDLE_RETRY_CUDA_SUCCESS(dynload::cublasLtCreate(&handle_));
+  }
+  const cublasLtHandle_t& GetCublasLtHandle() const { return handle_; }
+
+  ~CublasLtHandleHolder() PADDLE_MAY_THROW {
+    PADDLE_RETRY_CUDA_SUCCESS(dynload::cublasLtDestroy(handle_));
+  }
+
+  inline void Call(const std::function<void(blasLtHandle_t)>& callback) const {
+    std::lock_guard<std::mutex> guard(mtx_);
+    callback(handle_);
+  }
+
+ private:
+  DISABLE_COPY_AND_ASSIGN(CublasLtHandleHolder);
+
+  cublasLtHandle_t handle_;
+  mutable std::mutex mtx_;
+};
+
 }  // namespace platform
 }  // namespace paddle

paddle/fluid/platform/device/gpu/gpu_types.h

 // Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+// Copyright (c) 2022 NVIDIA Corporation. 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.
@@ -24,6 +25,7 @@
 #else
 #include <cuda_runtime.h>
 #include "paddle/fluid/platform/dynload/cublas.h"
+#include "paddle/fluid/platform/dynload/cublasLt.h"
 #include "paddle/fluid/platform/dynload/cudnn.h"
 #endif
 
@@ -70,6 +72,10 @@ DECLARE_TYPE_FOR_GPU(dnnHandle_t, cudnnHandle_t, miopenHandle_t);
 
 DECLARE_TYPE_FOR_GPU(blasHandle_t, cublasHandle_t, rocblas_handle);
 
+// TODO(Ming Huang): Since there is no blasLt handler,
+// use rocblas_handle for workround.
+DECLARE_TYPE_FOR_GPU(blasLtHandle_t, cublasLtHandle_t, rocblas_handle);
+
 using CUDAGraphID = unsigned long long;  // NOLINT
 
 #undef DECLARE_TYPE_FOR_GPU

paddle/fluid/platform/device_context.cc

 /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+Copyright (c) 2022 NVIDIA Corporation. 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
@@ -465,6 +467,9 @@ CUDAContext::CUDAContext(const CUDAPlace& place,
   InitCuBlasContext();
   InitCuDNNContext();
 #ifndef PADDLE_WITH_HIP
+#if CUDA_VERSION >= 11060
+  InitCuBlasLtContext();
+#endif
   InitCuSparseContext();
   InitCuSolverContext();
 #endif
@@ -476,6 +481,9 @@ void CUDAContext::SetStream(gpuStream_t stream) {
     DestoryCuDNNContext();
     DestoryCuBlasContext();
 #ifndef PADDLE_WITH_HIP
+#if CUDA_VERSION >= 11060
+    DestoryCuBlasLtContext();
+#endif
     DestoryCuSolverContext();
 #endif
 
@@ -485,6 +493,9 @@ void CUDAContext::SetStream(gpuStream_t stream) {
     InitCuBlasContext();
     InitCuDNNContext();
 #ifndef PADDLE_WITH_HIP
+#if CUDA_VERSION >= 11060
+    InitCuBlasLtContext();
+#endif
     InitCuSolverContext();
 #endif
   }
@@ -495,6 +506,9 @@ CUDAContext::~CUDAContext() {
   DestoryCuDNNContext();
   DestoryCuBlasContext();
 #ifndef PADDLE_WITH_HIP
+#if CUDA_VERSION >= 11060
+  InitCuBlasLtContext();
+#endif
   DestoryCuSparseContext();
   DestoryCuSolverContext();
 #endif
@@ -551,6 +565,14 @@ cublasHandle_t CUDADeviceContext::cublas_handle() const {
   }
   return phi::GPUContext::cublas_handle();
 }
+#if CUDA_VERSION >= 11060
+cublasLtHandle_t CUDADeviceContext::cublaslt_handle() const {
+  if (thread_ctx_.count(this)) {
+    return context()->CublasLtHandle()->GetCublasLtHandle();
+  }
+  return phi::GPUContext::cublaslt_handle();
+}
+#endif
 cusparseHandle_t CUDADeviceContext::cusparse_handle() const {
   if (thread_ctx_.count(this)) {
     return context()->CusparseHandle()->GetCusparseHandle();

paddle/fluid/platform/device_context.h

 /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+Copyright (c) 2022 NVIDIA Corporation. 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
@@ -29,6 +31,7 @@ limitations under the License. */
 #ifdef PADDLE_WITH_CUDA
 #include "paddle/fluid/platform/device/gpu/gpu_helper.h"
 #include "paddle/fluid/platform/dynload/cublas.h"
+#include "paddle/fluid/platform/dynload/cublasLt.h"
 #include "paddle/fluid/platform/dynload/cudnn.h"
 #include "paddle/fluid/platform/dynload/cusolver.h"
 #include "paddle/fluid/platform/dynload/cusparse.h"
@@ -332,6 +335,12 @@ class CUDAContext {
   }
 
 #ifndef PADDLE_WITH_HIP
+#if CUDA_VERSION >= 11060
+  const std::unique_ptr<CublasLtHandleHolder>& CublasLtHandle() const {
+    return cublaslt_handle_;
+  }
+#endif
+
   const std::unique_ptr<CusparseHandleHolder>& CusparseHandle() const {
     return cusparse_handle_;
   }
@@ -348,6 +357,14 @@ class CUDAContext {
   }
 
 #ifndef PADDLE_WITH_HIP
+#if CUDA_VERSION >= 11060
+  /*! \brief  Call cublasLt function safely. */
+  inline void CublasLtCall(
+      const std::function<void(blasLtHandle_t)>& callback) const {
+    cublaslt_handle_->Call(callback);
+  }
+#endif
+
   /*! \brief  Call cusparse function safely. */
   inline void CusparseCall(
       const std::function<void(phi::sparseHandle_t)>& callback) const {
@@ -394,6 +411,12 @@ class CUDAContext {
 #endif
 
 #ifndef PADDLE_WITH_HIP
+#if CUDA_VERSION >= 11060
+  void InitCuBlasLtContext() {
+    cublaslt_handle_.reset(new CublasLtHandleHolder());
+  }
+#endif
+
   void InitCuSparseContext() {
     cusparse_handle_.reset(new CusparseHandleHolder(RawStream()));
   }
@@ -472,6 +495,10 @@ class CUDAContext {
   }
 
 #ifndef PADDLE_WITH_HIP
+#if CUDA_VERSION >= 11060
+  void DestoryCuBlasLtContext() { cublaslt_handle_.reset(); }
+#endif
+
   void DestoryCuSparseContext() { cusparse_handle_.reset(); }
 #endif
 
@@ -497,6 +524,9 @@ class CUDAContext {
   std::unique_ptr<CublasHandleHolder> cublas_tensor_core_handle_;
   std::unique_ptr<CublasHandleHolder> cublas_tf32_tensor_core_handle_;
 #ifndef PADDLE_WITH_HIP
+#if CUDA_VERSION >= 11060
+  std::unique_ptr<CublasLtHandleHolder> cublaslt_handle_;
+#endif
   cusolverDnHandle_t cusolver_dn_handle_;
   std::unique_ptr<CusparseHandleHolder> cusparse_handle_;
 #endif
@@ -559,6 +589,7 @@ class CUDADeviceContext : public phi::GPUContext {
   rocblas_handle cublas_handle() const;
 #else
   cublasHandle_t cublas_handle() const;
+  cublasLtHandle_t cublaslt_handle() const;
   cusparseHandle_t cusparse_handle() const;
 #endif
 

paddle/fluid/pybind/pybind.cc

 /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+Copyright (c) 2022 NVIDIA 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.
@@ -3440,6 +3441,31 @@ All parameter, weight, gradient are variables in Paddle.
                         build_strategy = static.BuildStrategy()
                         build_strategy.fuse_elewise_add_act_ops = True
                      )DOC")
+      .def_property(
+          "fuse_gemm_epilogue",
+          [](const BuildStrategy &self) { return self.fuse_gemm_epilogue_; },
+          [](BuildStrategy &self, bool b) {
+            PADDLE_ENFORCE_NE(self.IsFinalized(), true,
+                              platform::errors::PreconditionNotMet(
+                                  "BuildStrategy has been finlaized, cannot be "
+                                  "configured again."));
+            self.fuse_gemm_epilogue_ = b;
+          },
+          R"DOC((bool, optional): fuse_gemm_epilogue indicate whether
+                to fuse matmul_op, elemenewist_add_op and activation_op,
+                it may make the execution faster. Default is False.
+
+                Examples:
+                    .. code-block:: python
+
+                        import paddle
+                        import paddle.static as static
+
+                        paddle.enable_static()
+
+                        build_strategy = static.BuildStrategy()
+                        build_strategy.fuse_gemm_epilogue = True
+                     )DOC")
       .def_property(
           "fuse_bn_act_ops",
           [](const BuildStrategy &self) { return self.fuse_bn_act_ops_; },

paddle/phi/backends/gpu/forwards.h

 /* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+Copyright (c) 2022 NVIDIA Corporation. 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.
@@ -56,6 +57,9 @@ using cudnnFusedOpsPlan_t = struct cudnnFusedOpsPlanStruct *;
 // Forward declaration of cuBLAS types.
 using cublasHandle_t = struct cublasContext *;
 
+// Forward declaration of cuBLASLt types.
+using cublasLtHandle_t = struct cublasLtContext *;
+
 // Forward declaration of cuSOLVER types.
 using cusolverDnHandle_t = struct cusolverDnContext *;
 

paddle/phi/backends/gpu/gpu_context.cc

 /* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+Copyright (c) 2022 NVIDIA Corporation. 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.
@@ -171,6 +172,7 @@ struct GPUContext::Impl {
     InitStream();
     InitEigenDevice();
     InitBlasHandle();
+    InitBlasLtHandle();
     InitDNNHandle();
     InitSolverHandle();
     InitSparseHandle();
@@ -183,6 +185,7 @@ struct GPUContext::Impl {
     InitGpuProperties();
     InitStream();
     InitBlasHandle();
+    InitBlasLtHandle();
     InitDNNHandle();
     InitSolverHandle();
     InitSparseHandle();
@@ -212,6 +215,7 @@ struct GPUContext::Impl {
     }
 #endif
     DestroyInternalBlasHandle();
+    DestroyInternalBlasLtHandle();
     DestoryInternalStream();
   }
 
@@ -418,6 +422,25 @@ struct GPUContext::Impl {
 
   void SetBlasHandle(blasHandle_t blas) { blas_handle_ = blas; }
 
+  void InitBlasLtHandle() {
+#if defined(PADDLE_WITH_CUDA) && CUDA_VERSION >= 11060
+    phi::dynload::cublasLtCreate(&blaslt_handle_);
+#endif
+  }
+
+  void DestroyInternalBlasLtHandle() {
+#if defined(PADDLE_WITH_CUDA) && CUDA_VERSION >= 11060
+    phi::dynload::cublasLtDestroy(blaslt_handle_);
+#endif
+  }
+
+  void SetBlasLtHandle(blasLtHandle_t blaslt) { blaslt_handle_ = blaslt; }
+
+  blasLtHandle_t GetBlasLtHandle() const {
+    PD_CHECK(blaslt_handle_ != nullptr, "the gpu blasLt handle is nullptr.");
+    return blaslt_handle_;
+  }
+
   void InitDNNHandle() {
     if (phi::dynload::HasCUDNN()) {
 #ifdef PADDLE_WITH_HIP
@@ -679,6 +702,7 @@ struct GPUContext::Impl {
   blasHandle_t blas_handle_{nullptr};
   blasHandle_t blas_tensor_core_handle_{nullptr};
   blasHandle_t blas_tf32_tensor_core_handle_{nullptr};
+  blasLtHandle_t blaslt_handle_{nullptr};
   dnnHandle_t dnn_handle_{nullptr};
   solverHandle_t solver_handle_{nullptr};
   sparseHandle_t sparse_handle_{nullptr};
@@ -725,6 +749,10 @@ blasHandle_t GPUContext::cublas_handle() const {
   return impl_->GetBlasHandle();
 }
 
+blasLtHandle_t GPUContext::cublaslt_handle() const {
+  return impl_->GetBlasLtHandle();
+}
+
 solverHandle_t GPUContext::cusolver_dn_handle() const {
   return impl_->GetSolverHandle();
 }
@@ -815,6 +843,10 @@ void GPUContext::SetBlasHandle(blasHandle_t blas) {
   impl_->SetBlasHandle(blas);
 }
 
+void GPUContext::SetBlasLtHandle(blasLtHandle_t blaslt) {
+  impl_->SetBlasLtHandle(blaslt);
+}
+
 void GPUContext::SetDnnHandle(dnnHandle_t handle) {
   impl_->SetDnnHandle(handle);
 }

paddle/phi/backends/gpu/gpu_context.h

 /* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+Copyright (c) 2022 NVIDIA Corporation. 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.
@@ -93,6 +94,9 @@ class GPUContext : public DeviceContext {
   /*! \brief  Return cublas handle in the device context. */
   blasHandle_t cublas_handle() const;
 
+  /*! \brief  Return cublasLt handle in the device context. */
+  blasLtHandle_t cublaslt_handle() const;
+
   /*! \brief  Return cusolver handle in the device context. */
   solverHandle_t cusolver_dn_handle() const;
 
@@ -193,6 +197,8 @@ class GPUContext : public DeviceContext {
 
   void SetBlasHandle(blasHandle_t);
 
+  void SetBlasLtHandle(blasLtHandle_t);
+
   void SetDnnHandle(dnnHandle_t);
 
   void SetSolverHandle(solverHandle_t);

paddle/phi/backends/gpu/gpu_decls.h

 // Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+// Copyright (c) 2022 NVIDIA Corporation. 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.
@@ -59,6 +60,10 @@ DECLARE_TYPE_FOR_GPU(dnnHandle_t, cudnnHandle_t, miopenHandle_t);
 
 DECLARE_TYPE_FOR_GPU(blasHandle_t, cublasHandle_t, rocblas_handle);
 
+// TODO(Ming Huang): Since there is no blasLt handler,
+// use rocblas_handle for workround.
+DECLARE_TYPE_FOR_GPU(blasLtHandle_t, cublasLtHandle_t, rocblas_handle);
+
 DECLARE_TYPE_FOR_GPU(solverHandle_t, cusolverDnHandle_t, rocsolver_handle);
 
 DECLARE_TYPE_FOR_GPU(sparseHandle_t, cusparseHandle_t, rocsparse_handle);

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -125,6 +125,17 @@ if(NOT WITH_GPU)
     LIST(REMOVE_ITEM TEST_OPS test_fused_attention_op)
     LIST(REMOVE_ITEM TEST_OPS test_fused_attention_op_api)
     LIST(REMOVE_ITEM TEST_OPS test_fused_transformer_encoder_layer)
+    LIST(REMOVE_ITEM TEST_OPS test_fused_gemm_epilogue_op)
+    LIST(REMOVE_ITEM TEST_OPS test_fused_gemm_epilogue_grad_op)
+    LIST(REMOVE_ITEM TEST_OPS test_fuse_gemm_epilogue_pass)
+endif()
+
+if (WITH_GPU)
+    if (CUDA_VERSION LESS 11.6)
+        LIST(REMOVE_ITEM TEST_OPS test_fused_gemm_epilogue_op)
+        LIST(REMOVE_ITEM TEST_OPS test_fused_gemm_epilogue_grad_op)
+        LIST(REMOVE_ITEM TEST_OPS test_fuse_gemm_epilogue_pass)
+    endif()
 endif()
 
 if(((NOT WITH_ROCM) AND (NOT WITH_GPU)) OR WIN32)

python/paddle/fluid/tests/unittests/test_fuse_gemm_epilogue_pass.py

+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2022 NVIDIA 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.
+"""Test cases for role makers."""
+
+from __future__ import print_function
+import paddle
+import os
+import unittest
+import numpy as np
+import paddle.fluid.core as core
+
+
+def compare(ref, res, atol, rtol):
+
+    ref = np.array(ref).flatten()
+    res = np.array(res).flatten()
+
+    tmp_ref = ref.astype(np.float)
+    tol = atol + rtol * abs(tmp_ref)
+
+    diff = abs(res - ref)
+
+    indices = np.transpose(np.where(diff > tol))
+    if len(indices) == 0:
+        return True
+    return False
+
+
+def verify_node_count(graph, node_name, target_count):
+    count = 0
+    for node in graph.nodes():
+        if node.name() == node_name:
+            count += 1
+    return count == target_count
+
+
+class MultiFCLayer(paddle.nn.Layer):
+    def __init__(self, hidden, Activation):
+        super(MultiFCLayer, self).__init__()
+        self.linear1 = paddle.nn.Linear(hidden, hidden)
+        self.linear2 = paddle.nn.Linear(hidden, hidden)
+        self.linear3 = paddle.nn.Linear(hidden, hidden)
+
+        self.relu1 = Activation()
+        self.relu2 = Activation()
+        self.relu3 = Activation()
+
+    def forward(self, x, matmul_y, ele_y):
+        output = self.linear1(x)
+        output = self.relu1(output)
+        output = self.linear2(output)
+
+        output1 = paddle.matmul(output, matmul_y)
+        output = self.linear3(output)
+        output = self.relu2(output)
+
+        output = paddle.matmul(output, matmul_y)
+        output = paddle.add(output, ele_y)
+        output = self.relu3(output)
+        output = paddle.add(output, output1)
+        return output
+
+
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueFWDBase(unittest.TestCase):
+    def setUp(self):
+        self.batch = 64
+        self.seqlen = 128
+        self.hidden = 768
+
+        paddle.enable_static()
+
+        self.main_prog = paddle.static.Program()
+        self.startup_prog = paddle.static.Program()
+
+        with paddle.static.program_guard(self.main_prog, self.startup_prog):
+            data = paddle.static.data(
+                name="_data",
+                shape=[-1, self.seqlen, self.hidden],
+                dtype='float32')
+            matmul_y = paddle.static.data(
+                name="_matmul_y",
+                shape=[1, self.hidden, self.hidden],
+                dtype='float32')
+            ele_y = paddle.static.data(
+                name="_ele_y", shape=[self.hidden, ], dtype='float32')
+
+            multi_layer = MultiFCLayer(self.hidden, self._get_act_type()[0])
+            with paddle.static.amp.fp16_guard():
+                out = multi_layer(data, matmul_y, ele_y)
+                self.loss = paddle.mean(out)
+
+        self.data_arr = np.random.random(
+            (self.batch, self.seqlen, self.hidden)).astype("float32") - 0.5
+        self.matmul_y_arr = np.random.random(
+            (1, self.hidden, self.hidden)).astype("float32") - 0.5
+        self.ele_y_arr = np.random.random(
+            (self.hidden, )).astype("float32") - 0.5
+
+        self.place = paddle.CUDAPlace(0)
+        self.exe = paddle.static.Executor(self.place)
+        self.exe.run(self.startup_prog)
+
+        self._pre_test_hooks()
+
+        self.feed = {
+            "_data": self.data_arr,
+            "_matmul_y": self.matmul_y_arr,
+            "_ele_y": self.ele_y_arr
+        }
+        self.reference = self.exe.run(self.main_prog,
+                                      feed=self.feed,
+                                      fetch_list=[self.loss.name])
+
+    @unittest.skipIf(not core.is_compiled_with_cuda(),
+                     "core is not compiled with CUDA")
+    def _test_output(self):
+        build_strategy = paddle.static.BuildStrategy()
+        build_strategy.fuse_gemm_epilogue = True
+        program = paddle.static.CompiledProgram(self.main_prog)
+        program = program.with_data_parallel(
+            loss_name=self.loss.name,
+            build_strategy=build_strategy,
+            places=paddle.static.cuda_places())
+
+        result = self.exe.run(program,
+                              feed=self.feed,
+                              fetch_list=[self.loss.name])
+        self.assertTrue(
+            compare(self.reference, result, self.atol, self.rtol),
+            "[{}] outputs are miss-matched.".format(type(self).__name__))
+        self.assertTrue(
+            verify_node_count(program._graph, "fused_gemm_epilogue", 3),
+            "[{}] The number of fused_gemm_epilogue is miss-matched in the computing graph.".
+            format(type(self).__name__))
+        act_fwd_name = self._get_act_type()[1]
+        self.assertTrue(
+            verify_node_count(program._graph, act_fwd_name, 1),
+            "[{}] The number of {} is miss-matched in the computing graph.".
+            format(type(self).__name__, act_fwd_name))
+
+    def _pre_test_hooks(self):
+        self.atol = 1e-4
+        self.rtol = 1e-3
+
+    def _get_act_type(self):
+        return paddle.nn.ReLU, "relu"
+
+
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueReluFWDFP32(TestFuseGemmEpilogueFWDBase):
+    def _pre_test_hooks(self):
+        self.atol = 1e-3
+        self.rtol = 1e-2
+
+    def _get_act_type(self):
+        return paddle.nn.ReLU, "relu"
+
+    def test_output(self):
+        self._test_output()
+
+
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueReluFWDFP16(TestFuseGemmEpilogueReluFWDFP32):
+    def _pre_test_hooks(self):
+        self.atol = 1e-3
+        self.rtol = 1e-2
+
+        fp16_var_list = paddle.static.amp.cast_model_to_fp16(self.main_prog)
+        paddle.static.amp.cast_parameters_to_fp16(
+            self.place, self.main_prog, to_fp16_var_names=fp16_var_list)
+
+        self.data_arr = self.data_arr.astype("float16")
+        self.matmul_y_arr = self.matmul_y_arr.astype("float16")
+        self.ele_y_arr = self.ele_y_arr.astype("float16")
+
+
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueGeluFWDFP32(TestFuseGemmEpilogueFWDBase):
+    def _pre_test_hooks(self):
+        self.atol = 1e-4
+        self.rtol = 1e-3
+
+    def _get_act_type(self):
+        return paddle.nn.GELU, "gelu"
+
+    def test_output(self):
+        self._test_output()
+
+
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueGeluFWDFP16(TestFuseGemmEpilogueGeluFWDFP32):
+    def _pre_test_hooks(self):
+        self.atol = 1e-3
+        self.rtol = 1e-2
+
+        fp16_var_list = paddle.static.amp.cast_model_to_fp16(self.main_prog)
+        paddle.static.amp.cast_parameters_to_fp16(
+            self.place, self.main_prog, to_fp16_var_names=fp16_var_list)
+
+        self.data_arr = self.data_arr.astype("float16")
+        self.matmul_y_arr = self.matmul_y_arr.astype("float16")
+        self.ele_y_arr = self.ele_y_arr.astype("float16")
+
+
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueBWDBase(unittest.TestCase):
+    def setUp(self):
+        self.batch = 64
+        self.seqlen = 128
+        self.hidden = 768
+
+        paddle.enable_static()
+
+        self.main_prog = paddle.static.Program()
+        self.startup_prog = paddle.static.Program()
+
+        with paddle.static.program_guard(self.main_prog, self.startup_prog):
+            data = paddle.static.data(
+                name="_data",
+                shape=[-1, self.seqlen, self.hidden],
+                dtype='float32')
+            matmul_y = paddle.static.data(
+                name="_matmul_y",
+                shape=[1, self.hidden, self.hidden],
+                dtype='float32')
+            ele_y = paddle.static.data(
+                name="_ele_y", shape=[self.hidden, ], dtype='float32')
+
+            multi_layer = MultiFCLayer(self.hidden, self._get_act_type()[0])
+            with paddle.static.amp.fp16_guard():
+                out = multi_layer(data, matmul_y, ele_y)
+                self.loss = paddle.mean(out)
+                paddle.static.append_backward(loss=self.loss)
+
+        self.data_arr = np.random.random(
+            (self.batch, self.seqlen, self.hidden)).astype("float32") - 0.5
+        self.matmul_y_arr = np.random.random(
+            (1, self.hidden, self.hidden)).astype("float32") - 0.5
+        self.ele_y_arr = np.random.random(
+            (self.hidden, )).astype("float32") - 0.5
+
+        self.place = paddle.CUDAPlace(0)
+        self.exe = paddle.static.Executor(self.place)
+        self.exe.run(self.startup_prog)
+
+        self._pre_test_hooks()
+
+        self.feed = {
+            "_data": self.data_arr,
+            "_matmul_y": self.matmul_y_arr,
+            "_ele_y": self.ele_y_arr
+        }
+
+        self.fetch = [
+            self.loss.name,
+            '{}.w_0@GRAD'.format(multi_layer.linear1.full_name()),
+            '{}.b_0@GRAD'.format(multi_layer.linear1.full_name()),
+            '{}.w_0@GRAD'.format(multi_layer.linear2.full_name()),
+            '{}.b_0@GRAD'.format(multi_layer.linear2.full_name()),
+            '{}.w_0@GRAD'.format(multi_layer.linear3.full_name()),
+            '{}.b_0@GRAD'.format(multi_layer.linear3.full_name())
+        ]
+        self.outs_ref = self.exe.run(self.main_prog,
+                                     feed=self.feed,
+                                     fetch_list=self.fetch)
+
+    @unittest.skipIf(not core.is_compiled_with_cuda(),
+                     "core is not compiled with CUDA")
+    def _test_output(self):
+        build_strategy = paddle.static.BuildStrategy()
+        build_strategy.fuse_gemm_epilogue = True
+        program = paddle.static.CompiledProgram(self.main_prog)
+        program = program.with_data_parallel(
+            loss_name=self.loss.name,
+            build_strategy=build_strategy,
+            places=paddle.static.cuda_places())
+
+        outs_res = self.exe.run(program, feed=self.feed, fetch_list=self.fetch)
+
+        for ref, res in zip(self.outs_ref, outs_res):
+            self.assertTrue(
+                compare(ref, res, self.atol, self.rtol),
+                "[{}] output is miss-matched.".format(type(self).__name__))
+
+        self.assertTrue(
+            verify_node_count(program._graph, "fused_gemm_epilogue", 3),
+            "[{}] The number of fused_gemm_epilogue is miss-matched in the computing graph.".
+            format(type(self).__name__))
+        self.assertTrue(
+            verify_node_count(program._graph, "fused_gemm_epilogue_grad", 3),
+            "[{}] The number of fused_gemm_epilogue_grad is miss-matched in the computing graph.".
+            format(type(self).__name__))
+        _, act_fwd_name, act_bwd_name = self._get_act_type()
+        self.assertTrue(
+            verify_node_count(program._graph, act_fwd_name, 1),
+            "[{}] The number of {} is miss-matched in the computing graph.".
+            format(type(self).__name__, act_fwd_name))
+        self.assertTrue(
+            verify_node_count(program._graph, act_bwd_name, 2),
+            "[{}] The number of {} is miss-matched in the computing graph.".
+            format(type(self).__name__, act_bwd_name))
+
+    def _pre_test_hooks(self):
+        self.atol = 1e-4
+        self.rtol = 1e-3
+
+    def _get_act_type(self):
+        return paddle.nn.ReLU, "relu", "relu_grad"
+
+
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueReLUBWDFP32(TestFuseGemmEpilogueBWDBase):
+    def _pre_test_hooks(self):
+        self.atol = 1e-4
+        self.rtol = 1e-3
+
+    def _get_act_type(self):
+        return paddle.nn.ReLU, "relu", "relu_grad"
+
+    def test_output(self):
+        self._test_output()
+
+
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueReLUBWDFP16(TestFuseGemmEpilogueReLUBWDFP32):
+    def _pre_test_hooks(self):
+        self.atol = 1e-3
+        self.rtol = 1e-2
+
+        fp16_var_list = paddle.static.amp.cast_model_to_fp16(self.main_prog)
+        paddle.static.amp.cast_parameters_to_fp16(
+            self.place, self.main_prog, to_fp16_var_names=fp16_var_list)
+
+        self.data_arr = self.data_arr.astype("float16")
+        self.matmul_y_arr = self.matmul_y_arr.astype("float16")
+        self.ele_y_arr = self.ele_y_arr.astype("float16")
+
+
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueGeLUBWDFP32(TestFuseGemmEpilogueBWDBase):
+    def _pre_test_hooks(self):
+        self.atol = 5e-4
+        self.rtol = 1e-3
+
+    def _get_act_type(self):
+        return paddle.nn.GELU, "gelu", "gelu_grad"
+
+    def test_output(self):
+        self._test_output()
+
+
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueGeLUBWDFP16(TestFuseGemmEpilogueGeLUBWDFP32):
+    def _pre_test_hooks(self):
+        self.atol = 1e-3
+        self.rtol = 1e-2
+
+        fp16_var_list = paddle.static.amp.cast_model_to_fp16(self.main_prog)
+        paddle.static.amp.cast_parameters_to_fp16(
+            self.place, self.main_prog, to_fp16_var_names=fp16_var_list)
+
+        self.data_arr = self.data_arr.astype("float16")
+        self.matmul_y_arr = self.matmul_y_arr.astype("float16")
+        self.ele_y_arr = self.ele_y_arr.astype("float16")
+
+
+if __name__ == "__main__":
+    np.random.seed(0)
+    unittest.main()

python/paddle/fluid/tests/unittests/test_fused_gemm_epilogue_grad_op.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2022 NVIDIA Corporation. 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
+import paddle.fluid.core as core
+from op_test import OpTest, skip_check_grad_ci
+
+
+def get_outputs(DOut, X, Y):
+    DX = np.dot(DOut, Y.T)
+    DY = np.dot(X.T, DOut)
+    DBias = np.sum(DOut, axis=0)
+
+    return DX, DY, DBias
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueGradOpDXYBiasFP16(OpTest):
+    def setUp(self):
+        self.op_type = "fused_gemm_epilogue_grad"
+        self.place = core.CUDAPlace(0)
+        self.init_dtype_type()
+
+        self.inputs = {
+            'DOut': np.random.random((8, 128)).astype(self.dtype) - 0.5,
+            'X': np.random.random((8, 4)).astype(self.dtype) - 0.5,
+            'Y': np.random.random((4, 128)).astype(self.dtype) - 0.5
+        }
+
+        self.attrs = {"activation": 'none'}
+
+        DX, DY, DBias = get_outputs(self.inputs['DOut'], self.inputs['X'],
+                                    self.inputs['Y'])
+        self.outputs = {'DX': DX, 'DY': DY, 'DBias': DBias}
+
+    def init_dtype_type(self):
+        self.dtype = np.float16
+        self.atol = 1e-3
+
+    def test_check_output(self):
+        if self.dtype == np.float16 and not core.is_float16_supported(
+                self.place):
+            return
+        self.check_output_with_place(self.place, atol=self.atol)
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueGradOpDXYBiasFP32(
+        TestFuseGemmEpilogueGradOpDXYBiasFP16):
+    def init_dtype_type(self):
+        self.dtype = np.single
+        self.atol = 1e-6
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueGradOpDXYBiasFP64(
+        TestFuseGemmEpilogueGradOpDXYBiasFP16):
+    def init_dtype_type(self):
+        self.dtype = np.double
+        self.atol = 1e-6
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueGradOpDYBiasFP16(OpTest):
+    def setUp(self):
+        self.op_type = "fused_gemm_epilogue_grad"
+        self.place = core.CUDAPlace(0)
+        self.init_dtype_type()
+
+        self.inputs = {
+            'DOut': np.random.random((8, 128)).astype(self.dtype) - 0.5,
+            'X': np.random.random((8, 4)).astype(self.dtype) - 0.5,
+            'Y': np.random.random((4, 128)).astype(self.dtype) - 0.5
+        }
+
+        self.attrs = {"activation": 'none'}
+
+        _, DY, DBias = get_outputs(self.inputs['DOut'], self.inputs['X'],
+                                   self.inputs['Y'])
+        self.outputs = {'DY': DY, 'DBias': DBias}
+
+    def init_dtype_type(self):
+        self.dtype = np.float16
+        self.atol = 1e-3
+
+    def test_check_output(self):
+        if self.dtype == np.float16 and not core.is_float16_supported(
+                self.place):
+            return
+        self.check_output_with_place(self.place, atol=self.atol)
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueGradOpDYBiasFP32(
+        TestFuseGemmEpilogueGradOpDYBiasFP16):
+    def init_dtype_type(self):
+        self.dtype = np.single
+        self.atol = 1e-6
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueGradOpDYBiasFP64(
+        TestFuseGemmEpilogueGradOpDYBiasFP16):
+    def init_dtype_type(self):
+        self.dtype = np.double
+        self.atol = 1e-6
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueGradOpDYFP16(OpTest):
+    def setUp(self):
+        self.op_type = "fused_gemm_epilogue_grad"
+        self.place = core.CUDAPlace(0)
+        self.init_dtype_type()
+
+        self.inputs = {
+            'DOut': np.random.random((8, 128)).astype(self.dtype) - 0.5,
+            'X': np.random.random((8, 4)).astype(self.dtype) - 0.5,
+            'Y': np.random.random((4, 128)).astype(self.dtype) - 0.5
+        }
+
+        self.attrs = {"activation": 'none'}
+
+        _, DY, _ = get_outputs(self.inputs['DOut'], self.inputs['X'],
+                               self.inputs['Y'])
+        self.outputs = {'DY': DY}
+
+    def init_dtype_type(self):
+        self.dtype = np.float16
+        self.atol = 1e-3
+
+    def test_check_output(self):
+        if self.dtype == np.float16 and not core.is_float16_supported(
+                self.place):
+            return
+        self.check_output_with_place(self.place, atol=self.atol)
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueGradOpDYFP32(TestFuseGemmEpilogueGradOpDYFP16):
+    def init_dtype_type(self):
+        self.dtype = np.single
+        self.atol = 1e-6
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueGradOpDYFP64(TestFuseGemmEpilogueGradOpDYFP16):
+    def init_dtype_type(self):
+        self.dtype = np.double
+        self.atol = 1e-6
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueGradOpDXYFP16(OpTest):
+    def setUp(self):
+        self.op_type = "fused_gemm_epilogue_grad"
+        self.place = core.CUDAPlace(0)
+        self.init_dtype_type()
+
+        self.inputs = {
+            'DOut': np.random.random((8, 128)).astype(self.dtype) - 0.5,
+            'X': np.random.random((8, 4)).astype(self.dtype) - 0.5,
+            'Y': np.random.random((4, 128)).astype(self.dtype) - 0.5
+        }
+
+        self.attrs = {"activation": 'none'}
+
+        DX, DY, _ = get_outputs(self.inputs['DOut'], self.inputs['X'],
+                                self.inputs['Y'])
+        self.outputs = {'DX': DX, 'DY': DY}
+
+    def init_dtype_type(self):
+        self.dtype = np.float16
+        self.atol = 1e-3
+
+    def test_check_output(self):
+        if self.dtype == np.float16 and not core.is_float16_supported(
+                self.place):
+            return
+        self.check_output_with_place(self.place, atol=self.atol)
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueGradOpDXYFP32(TestFuseGemmEpilogueGradOpDXYFP16):
+    def init_dtype_type(self):
+        self.dtype = np.single
+        self.atol = 1e-6
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueGradOpDXYFP64(TestFuseGemmEpilogueGradOpDXYFP16):
+    def init_dtype_type(self):
+        self.dtype = np.double
+        self.atol = 1e-6
+
+
+if __name__ == "__main__":
+    np.random.seed(0)
+    unittest.main()

python/paddle/fluid/tests/unittests/test_fused_gemm_epilogue_op.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2022 NVIDIA Corporation. 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
+import paddle.fluid.core as core
+from op_test import OpTest, skip_check_grad_ci
+
+
+def gelu(x):
+    y_ref = 0.5 * x * (
+        1.0 + np.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * np.power(x, 3))))
+    return y_ref.astype(x.dtype)
+
+
+def relu(x):
+    mask = x > 0
+    return x * mask
+
+
+def get_output(X, Y, bias, act):
+    out = np.dot(X, Y) + bias
+    if act == 'relu':
+        return relu(out)
+    elif act == 'gelu':
+        return gelu(out)
+    else:
+        return out
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueOpReluMMFP16(OpTest):
+    def setUp(self):
+        self.op_type = "fused_gemm_epilogue"
+        self.place = core.CUDAPlace(0)
+        self.init_dtype_type()
+
+        self.inputs = {
+            'X': np.random.random((8, 4)).astype(self.dtype) - 0.5,
+            'Y': np.random.random((4, 128)).astype(self.dtype) - 0.5,
+            'Bias': np.random.random((128, )).astype(self.dtype) - 0.5
+        }
+        self.outputs = {
+            'Out': get_output(self.inputs['X'], self.inputs['Y'],
+                              self.inputs['Bias'], 'relu')
+        }
+        self.attrs = {"activation": 'relu'}
+
+    def init_dtype_type(self):
+        self.dtype = np.float16
+        self.atol = 1e-3
+
+    def test_check_output(self):
+        if self.dtype == np.float16 and not core.is_float16_supported(
+                self.place):
+            return
+        self.check_output_with_place(self.place, atol=self.atol)
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueOpReluMMFP32(TestFuseGemmEpilogueOpReluMMFP16):
+    def init_dtype_type(self):
+        self.dtype = np.single
+        self.atol = 1e-6
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueOpReluMMFP64(TestFuseGemmEpilogueOpReluMMFP16):
+    def init_dtype_type(self):
+        self.dtype = np.double
+        self.atol = 1e-6
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueOpReluMTMFP16(OpTest):
+    def setUp(self):
+        self.op_type = "fused_gemm_epilogue"
+        self.place = core.CUDAPlace(0)
+        self.init_dtype_type()
+
+        self.inputs = {
+            'X': np.random.random((4, 8)).astype(self.dtype) - 0.5,
+            'Y': np.random.random((4, 128)).astype(self.dtype) - 0.5,
+            'Bias': np.random.random((128, )).astype(self.dtype) - 0.5
+        }
+        self.outputs = {
+            'Out': get_output(self.inputs['X'].T, self.inputs['Y'],
+                              self.inputs['Bias'], 'relu')
+        }
+        self.attrs = {'trans_x': True, "activation": 'relu'}
+
+    def init_dtype_type(self):
+        self.dtype = np.float16
+        self.atol = 1e-3
+
+    def test_check_output(self):
+        if self.dtype == np.float16 and not core.is_float16_supported(
+                self.place):
+            return
+        self.check_output_with_place(self.place, atol=self.atol)
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueOpReluMTMFP32(TestFuseGemmEpilogueOpReluMTMFP16):
+    def init_dtype_type(self):
+        self.dtype = np.single
+        self.atol = 1e-6
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueOpReluMTMFP64(TestFuseGemmEpilogueOpReluMTMFP16):
+    def init_dtype_type(self):
+        self.dtype = np.double
+        self.atol = 1e-6
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueOpReluMMTFP16(OpTest):
+    def setUp(self):
+        self.op_type = "fused_gemm_epilogue"
+        self.place = core.CUDAPlace(0)
+        self.init_dtype_type()
+
+        self.inputs = {
+            'X': np.random.random((8, 4)).astype(self.dtype) - 0.5,
+            'Y': np.random.random((128, 4)).astype(self.dtype) - 0.5,
+            'Bias': np.random.random((128, )).astype(self.dtype) - 0.5
+        }
+        self.outputs = {
+            'Out': get_output(self.inputs['X'], self.inputs['Y'].T,
+                              self.inputs['Bias'], 'relu')
+        }
+        self.attrs = {'trans_y': True, "activation": 'relu'}
+
+    def init_dtype_type(self):
+        self.dtype = np.float16
+        self.atol = 1e-3
+
+    def test_check_output(self):
+        if self.dtype == np.float16 and not core.is_float16_supported(
+                self.place):
+            return
+        self.check_output_with_place(self.place, atol=self.atol)
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueOpReluMMTFP32(TestFuseGemmEpilogueOpReluMMTFP16):
+    def init_dtype_type(self):
+        self.dtype = np.single
+        self.atol = 1e-6
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueOpReluMMTFP64(TestFuseGemmEpilogueOpReluMMTFP16):
+    def init_dtype_type(self):
+        self.dtype = np.double
+        self.atol = 1e-6
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueOpReluMTMTFP16(OpTest):
+    def setUp(self):
+        self.op_type = "fused_gemm_epilogue"
+        self.place = core.CUDAPlace(0)
+        self.init_dtype_type()
+
+        self.inputs = {
+            'X': np.random.random((4, 8)).astype(self.dtype) - 0.5,
+            'Y': np.random.random((128, 4)).astype(self.dtype) - 0.5,
+            'Bias': np.random.random((128, )).astype(self.dtype) - 0.5
+        }
+        self.outputs = {
+            'Out': get_output(self.inputs['X'].T, self.inputs['Y'].T,
+                              self.inputs['Bias'], 'relu')
+        }
+        self.attrs = {'trans_x': True, 'trans_y': True, "activation": 'relu'}
+
+    def init_dtype_type(self):
+        self.dtype = np.float16
+        self.atol = 1e-3
+
+    def test_check_output(self):
+        if self.dtype == np.float16 and not core.is_float16_supported(
+                self.place):
+            return
+        self.check_output_with_place(self.place, atol=self.atol)
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueOpReluMTMTFP32(TestFuseGemmEpilogueOpReluMTMTFP16):
+    def init_dtype_type(self):
+        self.dtype = np.single
+        self.atol = 1e-6
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueOpReluMTMTFP64(TestFuseGemmEpilogueOpReluMTMTFP16):
+    def init_dtype_type(self):
+        self.dtype = np.double
+        self.atol = 1e-6
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueOpReluMMFP16MultiDimX(OpTest):
+    def setUp(self):
+        self.op_type = "fused_gemm_epilogue"
+        self.place = core.CUDAPlace(0)
+        self.init_dtype_type()
+
+        self.inputs = {
+            'X': np.random.random((2, 2, 8, 4)).astype(self.dtype) - 0.5,
+            'Y': np.random.random((4, 128)).astype(self.dtype) - 0.5,
+            'Bias': np.random.random((128, )).astype(self.dtype) - 0.5
+        }
+        self.outputs = {
+            'Out': get_output(self.inputs['X'].reshape(
+                (-1, 4)), self.inputs['Y'], self.inputs['Bias'],
+                              'relu').reshape((2, 2, 8, 128))
+        }
+        self.attrs = {"activation": 'relu'}
+
+    def init_dtype_type(self):
+        self.dtype = np.float16
+        self.atol = 1e-3
+
+    def test_check_output(self):
+        if self.dtype == np.float16 and not core.is_float16_supported(
+                self.place):
+            return
+        self.check_output_with_place(self.place, atol=self.atol)
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueOpReluMMFP32MultiDimX(
+        TestFuseGemmEpilogueOpReluMMFP16MultiDimX):
+    def init_dtype_type(self):
+        self.dtype = np.single
+        self.atol = 1e-6
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueOpReluMMFP64MultiDimX(
+        TestFuseGemmEpilogueOpReluMMFP16MultiDimX):
+    def init_dtype_type(self):
+        self.dtype = np.double
+        self.atol = 1e-6
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueOpReluMTMFP16MultiDimX(OpTest):
+    def setUp(self):
+        self.op_type = "fused_gemm_epilogue"
+        self.place = core.CUDAPlace(0)
+        self.init_dtype_type()
+
+        self.inputs = {
+            'X': np.random.random((4, 2, 2, 8)).astype(self.dtype) - 0.5,
+            'Y': np.random.random((4, 128)).astype(self.dtype) - 0.5,
+            'Bias': np.random.random((128, )).astype(self.dtype) - 0.5
+        }
+        self.outputs = {
+            'Out': get_output(self.inputs['X'].reshape(
+                (4, -1)).T, self.inputs['Y'], self.inputs['Bias'],
+                              'relu').reshape((2, 2, 8, 128))
+        }
+        self.attrs = {'trans_x': True, "activation": 'relu'}
+
+    def init_dtype_type(self):
+        self.dtype = np.float16
+        self.atol = 1e-3
+
+    def test_check_output(self):
+        if self.dtype == np.float16 and not core.is_float16_supported(
+                self.place):
+            return
+        self.check_output_with_place(self.place, atol=self.atol)
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueOpReluMTMFP32MultiDimX(
+        TestFuseGemmEpilogueOpReluMTMFP16MultiDimX):
+    def init_dtype_type(self):
+        self.dtype = np.single
+        self.atol = 1e-6
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueOpReluMTMFP64MultiDimX(
+        TestFuseGemmEpilogueOpReluMTMFP16MultiDimX):
+    def init_dtype_type(self):
+        self.dtype = np.double
+        self.atol = 1e-6
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueOpGeluMMFP16(OpTest):
+    def setUp(self):
+        self.op_type = "fused_gemm_epilogue"
+        self.place = core.CUDAPlace(0)
+        self.init_dtype_type()
+
+        self.inputs = {
+            'X': np.random.random((8, 4)).astype(self.dtype) - 0.5,
+            'Y': np.random.random((4, 128)).astype(self.dtype) - 0.5,
+            'Bias': np.random.random((128, )).astype(self.dtype) - 0.5
+        }
+
+        self.attrs = {"activation": 'gelu'}
+
+        self.outputs = {
+            'Out': get_output(self.inputs['X'], self.inputs['Y'],
+                              self.inputs['Bias'], 'gelu')
+        }
+
+    def init_dtype_type(self):
+        self.dtype = np.float16
+        self.atol = 1e-3
+
+    def test_check_output(self):
+        if self.dtype == np.float16 and not core.is_float16_supported(
+                self.place):
+            return
+        self.check_output_with_place(self.place, atol=self.atol)
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueOpGeluMMFP32(TestFuseGemmEpilogueOpGeluMMFP16):
+    def init_dtype_type(self):
+        self.dtype = np.single
+        self.atol = 1e-6
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueOpGeluMMFP64(TestFuseGemmEpilogueOpGeluMMFP16):
+    def init_dtype_type(self):
+        self.dtype = np.double
+        self.atol = 1e-6
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueOpNoneMMFP16(OpTest):
+    def setUp(self):
+        self.op_type = "fused_gemm_epilogue"
+        self.place = core.CUDAPlace(0)
+        self.init_dtype_type()
+
+        self.inputs = {
+            'X': np.random.random((8, 4)).astype(self.dtype) - 0.5,
+            'Y': np.random.random((4, 128)).astype(self.dtype) - 0.5,
+            'Bias': np.random.random((128, )).astype(self.dtype) - 0.5
+        }
+
+        self.attrs = {"activation": 'none'}
+
+        self.outputs = {
+            'Out': get_output(self.inputs['X'], self.inputs['Y'],
+                              self.inputs['Bias'], 'none')
+        }
+
+    def init_dtype_type(self):
+        self.dtype = np.float16
+        self.atol = 1e-3
+
+    def test_check_output(self):
+        if self.dtype == np.float16 and not core.is_float16_supported(
+                self.place):
+            return
+        self.check_output_with_place(self.place, atol=self.atol)
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueOpNoneMMFP32(TestFuseGemmEpilogueOpNoneMMFP16):
+    def init_dtype_type(self):
+        self.dtype = np.single
+        self.atol = 1e-6
+
+
+@skip_check_grad_ci(reason="no grap op")
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFuseGemmEpilogueOpNoneMMFP64(TestFuseGemmEpilogueOpNoneMMFP16):
+    def init_dtype_type(self):
+        self.dtype = np.double
+        self.atol = 1e-6
+
+
+if __name__ == "__main__":
+    np.random.seed(0)
+    unittest.main()

tools/static_mode_white_list.py

 # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2022 NVIDIA Corporation. 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.
@@ -729,4 +730,6 @@ STATIC_MODE_TESTING_LIST = [
     'test_lu_op',
     'test_margin_cross_entropy_op',
     'test_pull_gpups_sparse_op',
+    'test_fused_gemm_epilogue_op',
+    'test_fused_gemm_epilogue_grad_op',
 ]