[WIP] Algorithm Cache of cuBlasLt Epilogue (#41010)

* Fix leading dimension setting error in fused_gemm_epilogue_grad_op.

* Add dyload to cuBlasLt functions.

* Added cublasLtMatmulAlgoGetHeuristic to improve performance.

* Added FLAGS_cublaslt_exhaustive_search_times to cublasLt epilogue

* Added UTs to FLAGS_cublaslt_exhaustive_search_times

* Added warmup runs in algo searching of Gemm epilogue.

* Update copyright and documents.

* Fixed error handling.

paddle/fluid/operators/fused/fused_gemm_epilogue_op.cu

@@ -15,6 +15,7 @@ limitations under the License. */
 
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/op_version_registry.h"
+#include "paddle/fluid/operators/fused/fused_gemm_epilogue_op.h"
 #include "paddle/fluid/platform/dynload/cublasLt.h"
 #include "paddle/fluid/platform/float16.h"
 
@@ -56,7 +57,6 @@ class FusedGemmEpilogueKernel : public framework::OpKernel<T> {
     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;
@@ -130,7 +130,7 @@ class FusedGemmEpilogueKernel : public framework::OpKernel<T> {
 
     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);
@@ -146,10 +146,26 @@ class FusedGemmEpilogueKernel : public framework::OpKernel<T> {
       beta = &beta32;
     }
 
+    const auto* y_data = y->data<T>();
+    const auto* x_data = x->data<T>();
+
+    cublasLtMatmulAlgo_t algo = GemmEpilogueAlgoCache::Instance().GetGemmAlgo(
+        lt_handle, operation_desc, y_desc, x_desc, out_desc, alpha, beta,
+        y_data, x_data, out_data, stream, workspace->ptr(), workspace_size);
+
     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));
+        lt_handle, operation_desc, alpha, y_data, y_desc, x_data, x_desc, beta,
+        out_data, out_desc, out_data, out_desc, &algo, workspace->ptr(),
+        workspace_size, stream));
+
+    PADDLE_ENFORCE_GPU_SUCCESS(
+        platform::dynload::cublasLtMatmulDescDestroy(operation_desc));
+    PADDLE_ENFORCE_GPU_SUCCESS(
+        platform::dynload::cublasLtMatrixLayoutDestroy(y_desc));
+    PADDLE_ENFORCE_GPU_SUCCESS(
+        platform::dynload::cublasLtMatrixLayoutDestroy(x_desc));
+    PADDLE_ENFORCE_GPU_SUCCESS(
+        platform::dynload::cublasLtMatrixLayoutDestroy(out_desc));
   }
 
  private:
@@ -205,7 +221,6 @@ class FusedGemmEpilogueGradKernel : public framework::OpKernel<T> {
     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;
@@ -215,7 +230,6 @@ class FusedGemmEpilogueGradKernel : public framework::OpKernel<T> {
 
     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;
@@ -262,8 +276,8 @@ class FusedGemmEpilogueGradKernel : public framework::OpKernel<T> {
                 &aux_data, sizeof(aux_data)));
         PADDLE_ENFORCE_GPU_SUCCESS(
             platform::dynload::cublasLtMatmulDescSetAttribute(
-                dx_operation_desc, CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_LD, &N,
-                sizeof(N)));
+                dx_operation_desc, CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_LD, &K,
+                sizeof(K)));
       }
 
       cublasLtMatrixLayout_t y_desc = NULL, dx_desc = NULL;
@@ -277,10 +291,24 @@ class FusedGemmEpilogueGradKernel : public framework::OpKernel<T> {
 
       dx->mutable_data<T>(ctx.GetPlace());
       auto* dx_data = dx->data<T>();
+      const auto* y_data = y->data<T>();
+      const auto* dout_data = dout->data<T>();
+
+      cublasLtMatmulAlgo_t algo = GemmEpilogueAlgoCache::Instance().GetGemmAlgo(
+          lt_handle, dx_operation_desc, y_desc, dout_desc, dx_desc, alpha, beta,
+          y_data, dout_data, dx_data, stream, dx_workspace->ptr(),
+          workspace_size);
+
       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));
+          &algo, dx_workspace->ptr(), workspace_size, stream));
+      PADDLE_ENFORCE_GPU_SUCCESS(
+          platform::dynload::cublasLtMatmulDescDestroy(dx_operation_desc));
+      PADDLE_ENFORCE_GPU_SUCCESS(
+          platform::dynload::cublasLtMatrixLayoutDestroy(y_desc));
+      PADDLE_ENFORCE_GPU_SUCCESS(
+          platform::dynload::cublasLtMatrixLayoutDestroy(dx_desc));
     }
 
     if (dy) {
@@ -324,11 +352,27 @@ class FusedGemmEpilogueGradKernel : public framework::OpKernel<T> {
 
       dy->mutable_data<T>(ctx.GetPlace());
       auto* dy_data = dy->data<T>();
+      const auto* dout_data = dout->data<T>();
+      const auto* x_data = x->data<T>();
+
+      cublasLtMatmulAlgo_t algo = GemmEpilogueAlgoCache::Instance().GetGemmAlgo(
+          lt_handle, dy_operation_desc, dout_desc, x_desc, dy_desc, alpha, beta,
+          dout_data, x_data, dy_data, stream, dy_workspace->ptr(),
+          workspace_size);
+
       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,
+          lt_handle, dy_operation_desc, alpha, dout_data, dout_desc, x_data,
+          x_desc, beta, dy_data, dy_desc, dy_data, dy_desc, &algo,
           dy_workspace->ptr(), workspace_size, stream));
+      PADDLE_ENFORCE_GPU_SUCCESS(
+          platform::dynload::cublasLtMatmulDescDestroy(dy_operation_desc));
+      PADDLE_ENFORCE_GPU_SUCCESS(
+          platform::dynload::cublasLtMatrixLayoutDestroy(x_desc));
+      PADDLE_ENFORCE_GPU_SUCCESS(
+          platform::dynload::cublasLtMatrixLayoutDestroy(dy_desc));
     }
+    PADDLE_ENFORCE_GPU_SUCCESS(
+        platform::dynload::cublasLtMatrixLayoutDestroy(dout_desc));
   }
 
  private:

paddle/fluid/operators/fused/fused_gemm_epilogue_op.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 <cuda_runtime_api.h>
+#include <algorithm>
+#include <mutex>
+#include <unordered_map>
+#include "gflags/gflags.h"
+#include "paddle/fluid/platform/dynload/cublasLt.h"
+#include "paddle/fluid/platform/float16.h"
+
+DECLARE_int64(cublaslt_exhaustive_search_times);
+
+namespace paddle {
+namespace operators {
+
+class GemmEpilogueAlgoCache {
+ public:
+  static GemmEpilogueAlgoCache &Instance() {
+    static GemmEpilogueAlgoCache instance(
+        FLAGS_cublaslt_exhaustive_search_times);
+    return instance;
+  }
+
+  GemmEpilogueAlgoCache(GemmEpilogueAlgoCache const &) = delete;
+  void operator=(GemmEpilogueAlgoCache const &) = delete;
+
+  cublasLtMatmulAlgo_t GetGemmAlgo(
+      cublasLtHandle_t lt_handle, cublasLtMatmulDesc_t op_desc,
+      cublasLtMatrixLayout_t a_desc, cublasLtMatrixLayout_t b_desc,
+      cublasLtMatrixLayout_t c_desc, const void *alpha, const void *beta,
+      const void *a, const void *b, void *c, cudaStream_t stream,
+      void *workspace, size_t workspace_size) {
+    int64_t seed = 0;
+    std::hash<int64_t> hash_fn;
+
+    HashMatmulDesc_(op_desc, &seed, hash_fn);
+    HashMatrixLayoutDesc_(a_desc, &seed, hash_fn);
+    HashMatrixLayoutDesc_(b_desc, &seed, hash_fn);
+    HashMatrixLayoutDesc_(c_desc, &seed, hash_fn);
+
+    cublasLtMatmulAlgo_t ret;
+    auto it = map_.end();
+    bool have_found = false;
+    {
+      std::lock_guard<std::mutex> lock(cache_mutex_);
+      it = map_.find(seed);
+
+      if (it != map_.end()) {
+        ret = it->second;
+        have_found = true;
+      }
+    }
+
+    if (!have_found) {
+      cublasLtMatmulPreference_t preference;
+      PADDLE_ENFORCE_GPU_SUCCESS(
+          platform::dynload::cublasLtMatmulPreferenceCreate(&preference));
+      PADDLE_ENFORCE_GPU_SUCCESS(
+          platform::dynload::cublasLtMatmulPreferenceSetAttribute(
+              preference, CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES,
+              &workspace_size, sizeof(workspace_size)));
+
+      int returned_results = 0;
+      cublasLtMatmulHeuristicResult_t heuristic_results[requested_algo_count_] =
+          {0};
+      PADDLE_ENFORCE_GPU_SUCCESS(
+          platform::dynload::cublasLtMatmulAlgoGetHeuristic(
+              lt_handle, op_desc, a_desc, b_desc, c_desc, c_desc, preference,
+              requested_algo_count_, heuristic_results, &returned_results));
+
+      PADDLE_ENFORCE_GT(
+          returned_results, 0,
+          platform::errors::Unavailable("No GEMM epilogue algorithm support!"));
+
+      PADDLE_ENFORCE_GPU_SUCCESS(
+          platform::dynload::cublasLtMatmulPreferenceDestroy(preference));
+
+      if (search_times_ > 0) {
+        int best_algo_idx = -1;
+        float best_algo_time = 0;
+
+        // Run 100 times for warmup
+        int warmup_algo_idx = 0;
+        for (int t = 0; t < 100; t++) {
+          cublasStatus_t status = platform::dynload::cublasLtMatmul(
+              lt_handle, op_desc, alpha, a, a_desc, b, b_desc, beta, c, c_desc,
+              c, c_desc, &heuristic_results[warmup_algo_idx].algo, workspace,
+              workspace_size, stream);
+          if (status != CUBLAS_STATUS_SUCCESS) {
+            t = -1;
+            warmup_algo_idx += 1;
+            if (warmup_algo_idx == requested_algo_count_) {
+              PADDLE_THROW(platform::errors::Unavailable(
+                  "No GEMM epilogue algorithm support!"));
+            }
+          }
+        }
+
+        cudaEvent_t start_event, stop_event;
+        PADDLE_ENFORCE_GPU_SUCCESS(cudaEventCreate(&start_event));
+        PADDLE_ENFORCE_GPU_SUCCESS(cudaEventCreate(&stop_event));
+
+        for (int algo_idx = 0; algo_idx < returned_results; ++algo_idx) {
+          float curr_time = 0;
+          for (int check_idx = 0; check_idx < search_times_; check_idx++) {
+            float time = 0;
+            PADDLE_ENFORCE_GPU_SUCCESS(cudaEventRecord(start_event, stream));
+
+            cublasStatus_t status = platform::dynload::cublasLtMatmul(
+                lt_handle, op_desc, alpha, a, a_desc, b, b_desc, beta, c,
+                c_desc, c, c_desc, &heuristic_results[algo_idx].algo, workspace,
+                workspace_size, stream);
+
+            PADDLE_ENFORCE_GPU_SUCCESS(cudaEventRecord(stop_event, stream));
+            PADDLE_ENFORCE_GPU_SUCCESS(cudaEventSynchronize(stop_event));
+            PADDLE_ENFORCE_GPU_SUCCESS(
+                cudaEventElapsedTime(&time, start_event, stop_event));
+            curr_time += time;
+            if (status != CUBLAS_STATUS_SUCCESS) {
+              curr_time = 3.40282e+038;  // Max Value of float
+              break;
+            }
+          }
+
+          curr_time = curr_time / search_times_;
+          if (curr_time < best_algo_time || algo_idx == 0) {
+            best_algo_idx = algo_idx;
+            best_algo_time = curr_time;
+          }
+        }
+
+        PADDLE_ENFORCE_GPU_SUCCESS(cudaEventDestroy(start_event));
+        PADDLE_ENFORCE_GPU_SUCCESS(cudaEventDestroy(stop_event));
+
+        if (best_algo_idx == -1) {
+          PADDLE_THROW(platform::errors::Unavailable(
+              "No GEMM epilogue algorithm support!"));
+        }
+
+        ret = heuristic_results[best_algo_idx].algo;
+      } else {
+        int decided_algo_idx = -1;
+        for (int algo_idx = 0; algo_idx < returned_results; ++algo_idx) {
+          cublasStatus_t status = platform::dynload::cublasLtMatmul(
+              lt_handle, op_desc, alpha, a, a_desc, b, b_desc, beta, c, c_desc,
+              c, c_desc, &heuristic_results[algo_idx].algo, workspace,
+              workspace_size, stream);
+          if (status == CUBLAS_STATUS_SUCCESS) {
+            decided_algo_idx = algo_idx;
+            break;
+          }
+        }
+        if (decided_algo_idx == -1) {
+          PADDLE_THROW(platform::errors::Unavailable(
+              "No GEMM epilogue algorithm support!"));
+        }
+        ret = heuristic_results[decided_algo_idx].algo;
+      }
+
+      std::lock_guard<std::mutex> lock(cache_mutex_);
+      map_[seed] = ret;
+    }
+
+    VLOG(4) << "Search time:" << search_times_ << ", Is hash-key (" << seed
+            << ") found in GemmEpilogueAlgoCache? " << have_found;
+
+    return ret;
+  }
+
+ private:
+  explicit GemmEpilogueAlgoCache(int search_times)
+      : search_times_(search_times) {
+    map_.clear();
+  }
+  std::unordered_map<int64_t, cublasLtMatmulAlgo_t> map_;
+  int search_times_;
+  const int requested_algo_count_ = 10;
+  std::mutex cache_mutex_;
+
+  void HashMatmulDesc_(cublasLtMatmulDesc_t desc, int64_t *seed,
+                       const std::hash<int64_t> &hash_fn) {
+    size_t size_to_write;
+    int trans_a, trans_b;
+    uint32_t epilogue;
+
+    PADDLE_ENFORCE_GPU_SUCCESS(
+        platform::dynload::cublasLtMatmulDescGetAttribute(
+            desc, CUBLASLT_MATMUL_DESC_TRANSA, &trans_a, sizeof(trans_a),
+            &size_to_write));
+    HashValue_(seed, hash_fn, static_cast<int64_t>(trans_a));
+
+    PADDLE_ENFORCE_GPU_SUCCESS(
+        platform::dynload::cublasLtMatmulDescGetAttribute(
+            desc, CUBLASLT_MATMUL_DESC_TRANSB, &trans_b, sizeof(trans_b),
+            &size_to_write));
+    HashValue_(seed, hash_fn, static_cast<int64_t>(trans_b));
+
+    PADDLE_ENFORCE_GPU_SUCCESS(
+        platform::dynload::cublasLtMatmulDescGetAttribute(
+            desc, CUBLASLT_MATMUL_DESC_EPILOGUE, &epilogue, sizeof(epilogue),
+            &size_to_write));
+    HashValue_(seed, hash_fn, static_cast<int64_t>(epilogue));
+  }
+
+  void HashMatrixLayoutDesc_(cublasLtMatrixLayout_t desc, int64_t *seed,
+                             const std::hash<int64_t> &hash_fn) {
+    size_t size_to_write;
+    uint32_t dtype;
+    int32_t batch;
+    uint64_t row, col;
+    int64_t ld, batch_offset;
+
+    PADDLE_ENFORCE_GPU_SUCCESS(
+        platform::dynload::cublasLtMatrixLayoutGetAttribute(
+            desc, CUBLASLT_MATRIX_LAYOUT_TYPE, &dtype, sizeof(dtype),
+            &size_to_write));
+    HashValue_(seed, hash_fn, static_cast<int64_t>(dtype));
+
+    PADDLE_ENFORCE_GPU_SUCCESS(
+        platform::dynload::cublasLtMatrixLayoutGetAttribute(
+            desc, CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &batch, sizeof(batch),
+            &size_to_write));
+    HashValue_(seed, hash_fn, static_cast<int64_t>(batch));
+
+    PADDLE_ENFORCE_GPU_SUCCESS(
+        platform::dynload::cublasLtMatrixLayoutGetAttribute(
+            desc, CUBLASLT_MATRIX_LAYOUT_ROWS, &row, sizeof(row),
+            &size_to_write));
+    HashValue_(seed, hash_fn, static_cast<int64_t>(row));
+
+    PADDLE_ENFORCE_GPU_SUCCESS(
+        platform::dynload::cublasLtMatrixLayoutGetAttribute(
+            desc, CUBLASLT_MATRIX_LAYOUT_COLS, &col, sizeof(col),
+            &size_to_write));
+    HashValue_(seed, hash_fn, static_cast<int64_t>(col));
+
+    PADDLE_ENFORCE_GPU_SUCCESS(
+        platform::dynload::cublasLtMatrixLayoutGetAttribute(
+            desc, CUBLASLT_MATRIX_LAYOUT_LD, &ld, sizeof(ld), &size_to_write));
+    HashValue_(seed, hash_fn, static_cast<int64_t>(ld));
+
+    PADDLE_ENFORCE_GPU_SUCCESS(
+        platform::dynload::cublasLtMatrixLayoutGetAttribute(
+            desc, CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, &batch_offset,
+            sizeof(batch_offset), &size_to_write));
+    HashValue_(seed, hash_fn, static_cast<int64_t>(batch_offset));
+  }
+
+  void HashValue_(int64_t *seed, const std::hash<int64_t> &hash_fn,
+                  int64_t value) {
+    *seed ^= hash_fn(value) + 0x9e3779b9 + (*seed << 6) + (*seed >> 2);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/platform/dynload/cublasLt.h

 /* Copyright (c) 2021 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.
@@ -45,9 +46,15 @@ namespace dynload {
   __macro(cublasLtMatmulDescCreate);             \
   __macro(cublasLtMatmulDescDestroy);            \
   __macro(cublasLtMatmulDescSetAttribute);       \
+  __macro(cublasLtMatmulDescGetAttribute);       \
   __macro(cublasLtMatrixLayoutCreate);           \
   __macro(cublasLtMatrixLayoutDestroy);          \
   __macro(cublasLtMatrixLayoutSetAttribute);     \
+  __macro(cublasLtMatrixLayoutGetAttribute);     \
+  __macro(cublasLtMatmulPreferenceCreate);       \
+  __macro(cublasLtMatmulPreferenceDestroy);      \
+  __macro(cublasLtMatmulPreferenceSetAttribute); \
+  __macro(cublasLtMatmulAlgoGetHeuristic);       \
   __macro(cublasLtMatrixTransform);              \
   __macro(cublasLtMatrixTransformDescCreate);    \
   __macro(cublasLtMatrixTransformDescDestroy);   \

paddle/fluid/platform/flags.cc

 // Copyright (c) 2019 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.
@@ -107,6 +108,29 @@ PADDLE_DEFINE_EXPORTED_string(
     "share-memory only.");
 #endif
 
+#if defined(PADDLE_WITH_CUDA)
+/**
+ * CUDA related FLAG
+ * Name: FLAGS_cublaslt_exhaustive_search_times
+ * Since Version: 2.3.0
+ * Value Range: int64_t, default=0
+ * Example:
+ * Note: Represents times of exhaustive search to evaluate performance of
+ *       cuBlasLt matmul algorithm (with/without epilogue). Set this flag
+ *       with value > 0 to enable exhaustive search. Default is 0, means
+ *       getting algorithms via heuristic search. There are two search methods
+ *       in cuBlasLt, heuristic search and exhaustive search. Exhaustive search
+ *       attempts all cuBlasLt algorithms to select the fastest, which is very
+ *       time-consuming, and the selected algorithm will be cached for a given
+ *       layer specification Once you change the layer specifications
+ *       (such as M, N and K), it will re-search again.
+ */
+PADDLE_DEFINE_EXPORTED_int64(
+    cublaslt_exhaustive_search_times, 0,
+    "The times of exhaustive search for cuBlasLt matmul with/without "
+    " epilogue algorithms, default is 0, means disabling exhaustive search.");
+#endif
+
 #if defined(PADDLE_WITH_ASCEND_CL)
 PADDLE_DEFINE_EXPORTED_string(
     selected_npus, "",

paddle/phi/backends/dynload/cublasLt.h

 /* Copyright (c) 2021 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.
@@ -59,9 +60,15 @@ extern void *cublasLt_dso_handle;
   __macro(cublasLtMatmulDescCreate);             \
   __macro(cublasLtMatmulDescDestroy);            \
   __macro(cublasLtMatmulDescSetAttribute);       \
+  __macro(cublasLtMatmulDescGetAttribute);       \
   __macro(cublasLtMatrixLayoutCreate);           \
   __macro(cublasLtMatrixLayoutDestroy);          \
   __macro(cublasLtMatrixLayoutSetAttribute);     \
+  __macro(cublasLtMatrixLayoutGetAttribute);     \
+  __macro(cublasLtMatmulPreferenceCreate);       \
+  __macro(cublasLtMatmulPreferenceDestroy);      \
+  __macro(cublasLtMatmulPreferenceSetAttribute); \
+  __macro(cublasLtMatmulAlgoGetHeuristic);       \
   __macro(cublasLtMatrixTransform);              \
   __macro(cublasLtMatrixTransformDescCreate);    \
   __macro(cublasLtMatrixTransformDescDestroy);   \

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

@@ -129,18 +129,11 @@ 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()
+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)
 
 if(((NOT WITH_ROCM) AND (NOT WITH_GPU)) OR WIN32)
     LIST(REMOVE_ITEM TEST_OPS test_c_comm_init_all_op)
@@ -644,6 +637,15 @@ py_test_modules(test_imperative_static_runner_mnist MODULES test_imperative_stat
     FLAGS_cudnn_deterministic=1)
 py_test_modules(test_imperative_static_runner_while MODULES test_imperative_static_runner_while ENVS
     FLAGS_cudnn_deterministic=1)
+
+if ((WITH_GPU) AND (CUDA_VERSION GREATER_EQUAL 11.6))
+    py_test_modules(test_fused_gemm_epilogue_op MODULES test_fused_gemm_epilogue_op)
+    py_test_modules(test_fused_gemm_epilogue_grad_op MODULES test_fused_gemm_epilogue_grad_op)
+    py_test_modules(test_fused_gemm_epilogue_op_with_es MODULES test_fused_gemm_epilogue_op ENVS FLAGS_cublaslt_exhaustive_search_times=30)
+    py_test_modules(test_fused_gemm_epilogue_grad_op_with_es MODULES test_fused_gemm_epilogue_grad_op ENVS FLAGS_cublaslt_exhaustive_search_times=30)
+    py_test_modules(test_fuse_gemm_epilogue_pass MODULES test_fuse_gemm_epilogue_pass)
+endif()
+
 set_tests_properties(test_conv2d_op PROPERTIES LABELS "RUN_TYPE=EXCLUSIVE")
 set_tests_properties(test_faster_tokenizer_op PROPERTIES LABELS "RUN_TYPE=EXCLUSIVE")
 set_tests_properties(test_conv2d_op_depthwise_conv PROPERTIES LABELS "RUN_TYPE=EXCLUSIVE")

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

@@ -49,8 +49,8 @@ def verify_node_count(graph, node_name, 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.linear1 = paddle.nn.Linear(hidden, 4 * hidden)
+        self.linear2 = paddle.nn.Linear(4 * hidden, hidden)
         self.linear3 = paddle.nn.Linear(hidden, hidden)
 
         self.relu1 = Activation()