Cudnn convolution reconstruction (#18284)

* rewrite the conv_op using cudnn_conv_helper

* add workspace limit for v7 test=develop

* fix test=develop

* add half float test=develop

* fix test=develop

* fix test=develop

* revise code style test=develop

* fix test=develop

paddle/fluid/operators/conv_cudnn_helper.h

@@ -14,11 +14,11 @@ limitations under the License. */
 
 #pragma once
 
+#include <memory>
 #include <vector>
 #include "paddle/fluid/framework/operator_kernel_configs.h"
 #include "paddle/fluid/operators/conv_cudnn_op_cache.h"
 #include "paddle/fluid/platform/cudnn_desc.h"
-
 namespace paddle {
 namespace operators {
 
@@ -57,16 +57,57 @@ struct SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t> {
                      bool deterministic, int algo_cache_id,
                      const framework::ExecutionContext& ctx) {
     auto dtype = platform::CudnnDataType<T>::type;
+    bool has_got_workspace_size = true;
     bool exhaustive = (exhaustive_search) & (dtype != CUDNN_DATA_HALF);
-
     size_t workspace_size_limit = FLAGS_conv_workspace_size_limit * 1024 * 1024;
-
+    size_t workspace_size = 0;
     algo_t algo;
+
+#if CUDA_VERSION >= 9000 && CUDNN_VERSION_MIN(7, 0, 1)
+    auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
+    if (dev_ctx.GetComputeCapability() >= 70 && dtype == CUDNN_DATA_HALF) {
+      CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
+          args.cdesc.desc(), CUDNN_TENSOR_OP_MATH));
+      VLOG(5) << "use cudnn_tensor_op_math";
+    } else {
+      CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
+          args.cdesc.desc(), CUDNN_DEFAULT_MATH));
+      VLOG(5) << "NOT use cudnn_tensor_op_math";
+    }
+#endif
+
     if (!exhaustive) {
+#if CUDNN_VERSION >= 7001
+      int perf_count;
+      int best_algo_idx = 0;
+      std::unique_ptr<perf_t[]> perf_results(new perf_t[kNUM_CUDNN_FWD_ALGS]);
+      CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm_v7(
+          args.handle, args.idesc.desc(), args.wdesc.desc(), args.cdesc.desc(),
+          args.odesc.desc(), kNUM_CUDNN_FWD_ALGS, &perf_count,
+          perf_results.get()));
+      algo = (perf_results.get())[best_algo_idx].algo;
+      workspace_size = GetWorkspaceSize(args, algo);
+
+      if (workspace_size > workspace_size_limit) {
+        has_got_workspace_size = false;
+        VLOG(1) << "Fallback to non-v7 method to find conv algorithm becasue "
+                   "the workspace size request("
+                << workspace_size << ") exceeds the limit("
+                << workspace_size_limit << ")";
+      }
+      if (!has_got_workspace_size) {
+        CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
+            args.handle, args.idesc.desc(), args.wdesc.desc(),
+            args.cdesc.desc(), args.odesc.desc(),
+            CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT, workspace_size_limit,
+            &algo));
+      }
+#else
       CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
           args.handle, args.idesc.desc(), args.wdesc.desc(), args.cdesc.desc(),
           args.odesc.desc(), CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
           workspace_size_limit, &algo));
+#endif
       VLOG(3) << "choose algo " << algo;
     } else {
       AlgorithmsCache<algo_t>& algo_cache =
@@ -128,15 +169,72 @@ struct SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t> {
                      const framework::ExecutionContext& ctx) {
     auto dtype = platform::CudnnDataType<T>::type;
     bool exhaustive = (exhaustive_search) & (dtype != CUDNN_DATA_HALF);
-
     size_t workspace_size_limit = FLAGS_conv_workspace_size_limit * 1024 * 1024;
-
+    size_t workspace_size = 0;
+    bool has_got_workspace_size = true;
     algo_t algo;
+
+#if CUDA_VERSION >= 9000 && CUDNN_VERSION_MIN(7, 0, 1)
+    auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
+    if (dev_ctx.GetComputeCapability() >= 70 && dtype == CUDNN_DATA_HALF) {
+      CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
+          args.cdesc.desc(), CUDNN_TENSOR_OP_MATH));
+      VLOG(5) << "use cudnn_tensor_op_math";
+    } else {
+      CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
+          args.cdesc.desc(), CUDNN_DEFAULT_MATH));
+      VLOG(5) << "NOT use cudnn_tensor_op_math";
+    }
+#endif
+
     if (!exhaustive && !deterministic) {
+#if CUDNN_VERSION >= 7001
+      int perf_count;
+      int best_algo_idx = 0;
+      std::unique_ptr<perf_t[]> perf_results(
+          new perf_t[kNUM_CUDNN_BWD_DATA_ALGS]);
+      CUDNN_ENFORCE(
+          platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm_v7(
+              args.handle, args.wdesc.desc(), args.odesc.desc(),
+              args.cdesc.desc(), args.idesc.desc(), kNUM_CUDNN_BWD_DATA_ALGS,
+              &perf_count, perf_results.get()));
+      algo = (perf_results.get())[best_algo_idx].algo;
+
+#if CUDNN_VERSION < 7500
+      int stride_dim = args.x->dims().size() - 2;
+      bool blacklist = std::any_of(args.s.begin(), args.s.begin() + stride_dim,
+                                   [=](int n) { return n != 1; });
+      if (blacklist && (static_cast<cudnnConvolutionBwdDataAlgo_t>(
+                            perf_results[best_algo_idx].algo) ==
+                            CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT_TILING ||
+                        static_cast<cudnnConvolutionBwdDataAlgo_t>(
+                            perf_results[best_algo_idx].algo) ==
+                            CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT)) {
+        algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1;
+      }
+#endif
+      workspace_size = GetWorkspaceSize(args, algo);
+      if (workspace_size > workspace_size_limit) {
+        has_got_workspace_size = false;
+        VLOG(1) << "Fallback to non-v7 method to find conv algorithm becasue "
+                   "the workspace size request("
+                << workspace_size << ") exceeds the limit("
+                << workspace_size_limit << ")";
+      }
+      if (!has_got_workspace_size) {
+        CUDNN_ENFORCE(
+            platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
+                args.handle, args.wdesc.desc(), args.odesc.desc(),
+                args.cdesc.desc(), args.idesc.desc(),
+                CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
+                workspace_size_limit, &algo));
+      }
+#else
       CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
-          args.handle, args.wdesc.desc(), args.idesc.desc(), args.cdesc.desc(),
-          args.odesc.desc(), CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
+          args.handle, args.wdesc.desc(), args.odesc.desc(), args.cdesc.desc(),
+          args.idesc.desc(), CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
           workspace_size_limit, &algo));
+#endif
     } else if (deterministic) {
       return CUDNN_CONVOLUTION_BWD_DATA_ALGO_1;
     } else {
@@ -186,8 +284,8 @@ struct SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t> {
     size_t workspace_size = 0;
     CUDNN_ENFORCE(
         platform::dynload::cudnnGetConvolutionBackwardDataWorkspaceSize(
-            args.handle, args.wdesc.desc(), args.idesc.desc(),
-            args.cdesc.desc(), args.odesc.desc(), algo, &workspace_size));
+            args.handle, args.wdesc.desc(), args.odesc.desc(),
+            args.cdesc.desc(), args.idesc.desc(), algo, &workspace_size));
     return workspace_size;
   }
 };
@@ -203,17 +301,61 @@ struct SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t> {
                      const framework::ExecutionContext& ctx) {
     auto dtype = platform::CudnnDataType<T>::type;
     bool exhaustive = (exhaustive_search) & (dtype != CUDNN_DATA_HALF);
-
     size_t workspace_size_limit = FLAGS_conv_workspace_size_limit * 1024 * 1024;
+    size_t workspace_size = 0;
+    bool has_got_workspace_size = true;
+
+#if CUDA_VERSION >= 9000 && CUDNN_VERSION_MIN(7, 0, 1)
+    auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
+    if (dev_ctx.GetComputeCapability() >= 70 && dtype == CUDNN_DATA_HALF) {
+      CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
+          args.cdesc.desc(), CUDNN_TENSOR_OP_MATH));
+      VLOG(5) << "use cudnn_tensor_op_math";
+    } else {
+      CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
+          args.cdesc.desc(), CUDNN_DEFAULT_MATH));
+      VLOG(5) << "NOT use cudnn_tensor_op_math";
+    }
+#endif
 
     algo_t algo;
     if (!exhaustive && !deterministic) {
+#if CUDNN_VERSION >= 7001
+      using perf_t = cudnnConvolutionBwdFilterAlgoPerf_t;
+      int perf_count;
+      int best_algo_idx = 0;
+      std::unique_ptr<perf_t[]> perf_results(
+          new perf_t[kNUM_CUDNN_BWD_FILTER_ALGS]);
+      CUDNN_ENFORCE(
+          platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm_v7(
+              args.handle, args.idesc.desc(), args.odesc.desc(),
+              args.cdesc.desc(), args.wdesc.desc(), kNUM_CUDNN_BWD_FILTER_ALGS,
+              &perf_count, perf_results.get()));
+      algo = (perf_results.get())[best_algo_idx].algo;
+      workspace_size = GetWorkspaceSize(args, algo);
+      if (workspace_size > workspace_size_limit) {
+        has_got_workspace_size = false;
+        VLOG(1) << "Fallback to non-v7 method to find conv algorithm becasue "
+                   "the workspace size request("
+                << workspace_size << ") exceeds the limit("
+                << workspace_size_limit << ")";
+      }
+      if (!has_got_workspace_size) {
+        CUDNN_ENFORCE(
+            platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
+                args.handle, args.idesc.desc(), args.odesc.desc(),
+                args.cdesc.desc(), args.wdesc.desc(),
+                CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
+                workspace_size_limit, &algo));
+      }
+#else
       CUDNN_ENFORCE(
           platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
               args.handle, args.idesc.desc(), args.odesc.desc(),
               args.cdesc.desc(), args.wdesc.desc(),
               CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
               workspace_size_limit, &algo));
+#endif
     } else if (deterministic) {
       return CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1;
     } else {

paddle/fluid/operators/conv_cudnn_op.cu.cc

@@ -79,44 +79,42 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
     std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
     std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
     int groups = ctx.Attr<int>("groups");
-    int64_t user_workspace_size =
-        static_cast<size_t>(ctx.Attr<int>("workspace_size_MB"));
     bool exhaustive_search =
         FLAGS_cudnn_exhaustive_search || ctx.Attr<bool>("exhaustive_search");
 
+    if (exhaustive_search && FLAGS_cudnn_deterministic) {
+      PADDLE_THROW(
+          "Cann't set exhaustive_search True and "
+          "FLAGS_cudnn_deterministic True at same time.");
+    }
+
     const T* input_data = input->data<T>();
     const T* filter_data = filter->data<T>();
     T* output_data = output->mutable_data<T>(ctx.GetPlace());
-
     // ------------------- cudnn descriptors ---------------------
-    ScopedTensorDescriptor input_desc;
-    ScopedTensorDescriptor output_desc;
-    ScopedFilterDescriptor filter_desc;
-    ScopedConvolutionDescriptor conv_desc;
+    ConvArgs args{input, filter, output, strides, paddings, dilations};
+    auto handle = dev_ctx.cudnn_handle();
+    auto workspace_handle = dev_ctx.cudnn_workspace_handle();
+    auto dtype = platform::CudnnDataType<T>::type;
     DataLayout layout = DataLayout::kNCHW;
     if (input->dims().size() == 5) {
       layout = DataLayout::kNCDHW;
     }
+    auto layout_format = GetCudnnTensorFormat(layout);
 
-    cudnnConvolutionDescriptor_t cudnn_conv_desc =
-        conv_desc.descriptor<T>(paddings, strides, dilations);
-
+    args.handle = handle;
+    args.cdesc.set(dtype, paddings, strides, dilations);
 #if CUDNN_VERSION_MIN(7, 0, 1)
     // cudnn 7 can support groups, no need to do it manually
     // FIXME(typhoonzero): find a better way to disable groups
     // rather than setting it to 1.
     CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionGroupCount(
-        cudnn_conv_desc, groups));
+        args.cdesc.desc(), groups));
     groups = 1;
 #endif
-
-    cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
-        layout, framework::vectorize2int(input->dims()), groups);
-    cudnnTensorDescriptor_t cudnn_output_desc = output_desc.descriptor<T>(
-        layout, framework::vectorize2int(output->dims()), groups);
-    cudnnFilterDescriptor_t cudnn_filter_desc = filter_desc.descriptor<T>(
-        layout, framework::vectorize2int(filter->dims()), groups);
-
+    args.idesc.set(*input, groups);
+    args.wdesc.set(*filter, layout_format, groups);
+    args.odesc.set(*output, groups);
     int i_n, i_c, i_d, i_h, i_w;
     GetNCDHW(input->dims(), DataLayout::kNCHW, &i_n, &i_c, &i_d, &i_h, &i_w);
     int o_n, o_c, o_d, o_h, o_w;
@@ -126,149 +124,27 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
     int group_offset_out = o_c / groups * o_h * o_w * o_d;
     int group_offset_filter = filter->numel() / groups;
     // ------------------- cudnn conv workspace ---------------------
-    size_t workspace_size_in_bytes;  // final workspace to allocate.
-    size_t workspace_size_limit = 0;
-    if (FLAGS_conv_workspace_size_limit > 0 || user_workspace_size > 0) {
-      int64_t max_user_size =
-          std::min(static_cast<int64_t>(FLAGS_conv_workspace_size_limit),
-                   user_workspace_size);
-      workspace_size_limit = max_user_size * 1024 * 1024;
-    }
-
+    size_t workspace_size = 0;  // final workspace to allocate.
     // ------------------- cudnn conv algorithm ---------------------
     cudnnConvolutionFwdAlgo_t algo{};
-    bool half_float = false;
-
-#if CUDA_VERSION >= 9000 && CUDNN_VERSION_MIN(7, 0, 1)
-    // Tensor core is supported since the volta GPU and
-    // is only enabled when input and filter data are float16
-    if (dev_ctx.GetComputeCapability() >= 70 &&
-        std::type_index(typeid(T)) ==
-            std::type_index(typeid(platform::float16))) {
-      CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
-          cudnn_conv_desc, CUDNN_TENSOR_OP_MATH));
-      // Currently tensor core is only enabled using this algo
-      algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM;
-      half_float = true;
-      VLOG(5) << "use cudnn_tensor_op_math";
-    } else {
-      CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
-          cudnn_conv_desc, CUDNN_DEFAULT_MATH));
-      VLOG(5) << "NOT use cudnn_tensor_op_math";
-    }
-#endif
 
-    auto handle = dev_ctx.cudnn_handle();
-    auto workspace_handle = dev_ctx.cudnn_workspace_handle();
-    auto x_dims = framework::vectorize(input->dims());
-    auto f_dims = framework::vectorize(filter->dims());
-
-    // TODO(dangqingqing) simplify the following code by SearchAlgorithm in
-    // conv_cudnn_helper.h
-    bool has_got_workspace_size = false;
-    if ((!exhaustive_search) && (!half_float)) {
-#if CUDNN_VERSION >= 7001
-      using perf_t = cudnnConvolutionFwdAlgoPerf_t;
-      int perf_count;
-      int best_algo_idx = 0;
-      std::unique_ptr<perf_t[]> perf_results(new perf_t[kNUM_CUDNN_FWD_ALGS]);
-      CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm_v7(
-          handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
-          cudnn_output_desc, kNUM_CUDNN_FWD_ALGS, &perf_count,
-          perf_results.get()));
-      algo = (perf_results.get())[best_algo_idx].algo;
-
-      // get workspace size able to allocate
-      CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
-          handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
-          cudnn_output_desc, algo, &workspace_size_in_bytes));
-
-      // NOTE(zjl): cudnnGetConvolutionForwardAlgorithm_v7 cannot limit
-      // workspace size. If the workspace size found by v7 exceeds the limit,
-      // we should fallback to non-v7 method to find another algorithm.
-      if (workspace_size_in_bytes > workspace_size_limit) {
-        VLOG(1) << "Fallback to non-v7 method to find conv algorithm becasue "
-                   "the workspace size request("
-                << workspace_size_in_bytes << ") exceeds the limit("
-                << workspace_size_limit << ")";
-#endif
-        CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
-            handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
-            cudnn_output_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
-            workspace_size_limit, &algo));
-#if CUDNN_VERSION >= 7001
-      } else {
-        has_got_workspace_size = true;
-      }
-#endif
+    using search = SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t>;
+    algo = search::Find<T>(args, exhaustive_search, false, 0, ctx);
+    workspace_size = search::GetWorkspaceSize(args, algo);
 
-      VLOG(3) << "cuDNN forward algo " << algo;
-    } else if (exhaustive_search && (!half_float)) {
-      AlgorithmsCache<cudnnConvolutionFwdAlgo_t>& algo_cache =
-          ctx.GetKernelConfig<AlgorithmsCache<cudnnConvolutionFwdAlgo_t>>(0);
-
-      algo = algo_cache.GetAlgorithm(
-          x_dims, f_dims, strides, paddings, dilations, 0, [&]() {
-            int returned_algo_count;
-            std::array<cudnnConvolutionFwdAlgoPerf_t, kNUM_CUDNN_FWD_ALGS>
-                fwd_perf_stat;
-
-            auto cudnn_find_func = [&](void* cudnn_workspace) {
-              CUDNN_ENFORCE(
-                  platform::dynload::cudnnFindConvolutionForwardAlgorithmEx(
-                      handle, cudnn_input_desc, input_data, cudnn_filter_desc,
-                      filter_data, cudnn_conv_desc, cudnn_output_desc,
-                      output_data, kNUM_CUDNN_FWD_ALGS, &returned_algo_count,
-                      fwd_perf_stat.data(), cudnn_workspace,
-                      workspace_size_limit));
-            };
-            workspace_handle.RunFuncSync(cudnn_find_func, workspace_size_limit);
-
-            VLOG(3) << "Perf result: (algo: stat, time, memory)";
-            for (int i = 0; i < returned_algo_count; ++i) {
-              const auto& stat = fwd_perf_stat[i];
-              VLOG(3) << stat.algo << ": " << stat.status << " " << stat.time
-                      << " " << stat.memory;
-            }
-            return fwd_perf_stat[0].algo;
-          });
-      VLOG(3) << "choose algo " << algo;
-    } else {
-      PADDLE_ENFORCE(half_float,
-                     "cuDNN exhaustive search doesn't support half float.");
-    }
-
-    if (!has_got_workspace_size) {
-      // get workspace size able to allocate
-      CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
-          handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
-          cudnn_output_desc, algo, &workspace_size_in_bytes));
-    }
-
-    // It is possible for float16 on Volta GPU to allocate more memory than
-    // the limit because the algo is overrided to use tensor core.
-    PADDLE_ENFORCE_LE(workspace_size_in_bytes, workspace_size_limit,
-                      "workspace_size to be allocated exceeds the limit");
-
-    // Allocate on GPU memory
-    Tensor cudnn_workspace =
-        ctx.AllocateTmpTensor<int8_t, platform::CUDADeviceContext>(
-            framework::make_ddim(
-                {static_cast<int64_t>(workspace_size_in_bytes)}),
-            dev_ctx);
-    void* cudnn_workspace_ptr =
-        static_cast<void*>(cudnn_workspace.data<int8_t>());
-    VLOG(2) << "Cudnn workspace size fwd: "
-            << static_cast<double>(workspace_size_in_bytes) / (1 << 20)
-            << " MB";
     // ------------------- cudnn conv forward ---------------------
     ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
     for (int i = 0; i < groups; i++) {
-      CUDNN_ENFORCE(platform::dynload::cudnnConvolutionForward(
-          handle, &alpha, cudnn_input_desc, input_data + i * group_offset_in,
-          cudnn_filter_desc, filter_data + i * group_offset_filter,
-          cudnn_conv_desc, algo, cudnn_workspace_ptr, workspace_size_in_bytes,
-          &beta, cudnn_output_desc, output_data + i * group_offset_out));
+      workspace_handle.RunFunc(
+          [&](void* workspace_ptr) {
+            CUDNN_ENFORCE(platform::dynload::cudnnConvolutionForward(
+                handle, &alpha, args.idesc.desc(),
+                input_data + i * group_offset_in, args.wdesc.desc(),
+                filter_data + i * group_offset_filter, args.cdesc.desc(), algo,
+                workspace_ptr, workspace_size, &beta, args.odesc.desc(),
+                output_data + i * group_offset_out));
+          },
+          workspace_size);
     }
   }
 };
@@ -294,62 +170,30 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
     std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
     std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
     int groups = ctx.Attr<int>("groups");
-    int64_t user_workspace_size =
-        static_cast<size_t>(ctx.Attr<int>("workspace_size_MB"));
     bool exhaustive_search =
         FLAGS_cudnn_exhaustive_search || ctx.Attr<bool>("exhaustive_search");
-    if (exhaustive_search && FLAGS_cudnn_deterministic) {
+    bool deterministic = FLAGS_cudnn_deterministic;
+    if (exhaustive_search && deterministic) {
       PADDLE_THROW(
           "Can't set exhaustive_search True and "
           "FLAGS_cudnn_deterministic True at same time.");
     }
 
-    // ------------------- cudnn descriptors ---------------------
-    ScopedTensorDescriptor input_desc;
-    ScopedTensorDescriptor output_grad_desc;
-
-    ScopedFilterDescriptor filter_desc;
-    ScopedFilterDescriptor filter_grad_desc;
-    ScopedConvolutionDescriptor conv_desc;
+    T* filter_grad_data = nullptr;
+    T* input_grad_data = nullptr;
+    ConvArgs args1{input_grad, filter,   output_grad,
+                   strides,    paddings, dilations};
+    ConvArgs args2{input,   filter_grad, output_grad,
+                   strides, paddings,    dilations};
+    // conv_cudnn_helper.h
+    auto handle = dev_ctx.cudnn_handle();
+    auto dtype = platform::CudnnDataType<T>::type;
     DataLayout layout = DataLayout::kNCHW;
     if (input->dims().size() == 5) {
       layout = DataLayout::kNCDHW;
     }
-
-    cudnnConvolutionDescriptor_t cudnn_conv_desc =
-        conv_desc.descriptor<T>(paddings, strides, dilations);
-
-#if CUDNN_VERSION_MIN(7, 0, 1)
-    // cudnn 7 can support groups, no need to do it manually
-    // FIXME(typhoonzero): find a better way to disable groups
-    // rather than setting it to 1.
-    CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionGroupCount(
-        cudnn_conv_desc, groups));
-    groups = 1;
-#endif
-
-    cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
-        layout, framework::vectorize2int(input->dims()), groups);
-    cudnnTensorDescriptor_t cudnn_output_grad_desc =
-        output_grad_desc.descriptor<T>(
-            layout, framework::vectorize2int(output_grad->dims()), groups);
-    cudnnFilterDescriptor_t cudnn_filter_desc = filter_desc.descriptor<T>(
-        layout, framework::vectorize2int(filter->dims()), groups);
-
-#if CUDA_VERSION >= 9000 && CUDNN_VERSION_MIN(7, 0, 1)
-    // Enable Tensor Core for cudnn backward
-    if (dev_ctx.GetComputeCapability() >= 70 &&
-        std::type_index(typeid(T)) ==
-            std::type_index(typeid(platform::float16))) {
-      CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
-          cudnn_conv_desc, CUDNN_TENSOR_OP_MATH));
-      VLOG(5) << "use cudnn_tensor_op_math for backward";
-    } else {
-      CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
-          cudnn_conv_desc, CUDNN_DEFAULT_MATH));
-      VLOG(5) << "NOT use cudnn_tensor_op_math for backward";
-    }
-#endif
+    auto layout_tensor = GetCudnnTensorFormat(layout);
+    auto workspace_handle = dev_ctx.cudnn_workspace_handle();
 
     int i_n, i_c, i_d, i_h, i_w;
     GetNCDHW(input->dims(), DataLayout::kNCHW, &i_n, &i_c, &i_d, &i_h, &i_w);
@@ -361,263 +205,83 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
     int group_offset_out = o_c / groups * o_h * o_w * o_d;
     int group_offset_filter = filter->numel() / groups;
     // ------------------- cudnn backward algorithm ---------------------
-    cudnnConvolutionBwdDataAlgo_t data_algo{};
-    cudnnConvolutionBwdFilterAlgo_t filter_algo{};
-    size_t workspace_size_in_bytes = 0, tmp_size = 0;
-    size_t workspace_size_limit = 0;
-    if (FLAGS_conv_workspace_size_limit > 0 || user_workspace_size > 0) {
-      int64_t max_user_size =
-          std::min(static_cast<int64_t>(FLAGS_conv_workspace_size_limit),
-                   user_workspace_size);
-      workspace_size_limit = max_user_size * 1024 * 1024;
-    }
-
-    Tensor cudnn_workspace;
-    void* cudnn_workspace_ptr = nullptr;
-    if ((input_data || filter_data) && exhaustive_search) {
-      cudnn_workspace =
-          ctx.AllocateTmpTensor<int8_t, platform::CUDADeviceContext>(
-              framework::make_ddim(
-                  {static_cast<int64_t>(workspace_size_limit)}),
-              dev_ctx);
-      cudnn_workspace_ptr = static_cast<void*>(cudnn_workspace.data<int8_t>());
-    }
+    cudnnConvolutionBwdDataAlgo_t data_algo =
+        static_cast<cudnnConvolutionBwdDataAlgo_t>(0);
+    cudnnConvolutionBwdFilterAlgo_t filter_algo =
+        static_cast<cudnnConvolutionBwdFilterAlgo_t>(0);
+    size_t workspace_size = 0;
+    int iwo_groups, c_groups;
 
-    // TODO(dangqingqing) simplify the following code by SearchAlgorithm in
-    // conv_cudnn_helper.h
-    auto x_dims = framework::vectorize(input->dims());
-    auto f_dims = framework::vectorize(filter->dims());
-    auto handle = dev_ctx.cudnn_handle();
+#if CUDNN_VERSION_MIN(7, 0, 1)
+    iwo_groups = 1;
+    c_groups = groups;
+    groups = 1;
+#endif
 
-    bool has_got_bwd_data_ws_size = false;
     if (input_grad) {
-      T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
-      if (exhaustive_search) {
-        AlgorithmsCache<cudnnConvolutionBwdDataAlgo_t>& data_algo_cache =
-            ctx.GetKernelConfig<AlgorithmsCache<cudnnConvolutionBwdDataAlgo_t>>(
-                0);
-
-        data_algo = data_algo_cache.GetAlgorithm(
-            x_dims, f_dims, strides, paddings, dilations, 0, [&]() {
-              int returned_algo_count;
-              std::array<cudnnConvolutionBwdDataAlgoPerf_t,
-                         kNUM_CUDNN_BWD_DATA_ALGS>
-                  data_perf_stat;
-
-              CUDNN_ENFORCE(platform::dynload::
-                                cudnnFindConvolutionBackwardDataAlgorithmEx(
-                                    handle, cudnn_filter_desc, filter_data,
-                                    cudnn_output_grad_desc, output_grad_data,
-                                    cudnn_conv_desc, cudnn_input_desc,
-                                    input_grad_data, kNUM_CUDNN_BWD_DATA_ALGS,
-                                    &returned_algo_count, data_perf_stat.data(),
-                                    cudnn_workspace_ptr, workspace_size_limit));
-
-              VLOG(3) << "Perf result: (algo: stat, time, memory)";
-              for (int i = 0; i < returned_algo_count; ++i) {
-                const auto& stat = data_perf_stat[i];
-                VLOG(3) << stat.algo << ": " << stat.status << " " << stat.time
-                        << " " << stat.memory;
-              }
-              return data_perf_stat[0].algo;
-            });
-        VLOG(3) << "cuDNN backward data algo " << data_algo;
-      } else if (FLAGS_cudnn_deterministic) {
-        data_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1;
-      } else {
-#if CUDNN_VERSION >= 7001
-        using perf_t = cudnnConvolutionBwdDataAlgoPerf_t;
-        int perf_count;
-        int best_algo_idx = 0;
-        std::unique_ptr<perf_t[]> perf_results(
-            new perf_t[kNUM_CUDNN_BWD_DATA_ALGS]);
-        CUDNN_ENFORCE(
-            platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm_v7(
-                handle, cudnn_filter_desc,
-                // dyDesc: Handle to the previously initialized input
-                // differential
-                // tensor descriptor.
-                cudnn_output_grad_desc, cudnn_conv_desc,
-                // dxDesc: Handle to the previously initialized output tensor
-                // descriptor.
-                cudnn_input_desc, kNUM_CUDNN_BWD_DATA_ALGS, &perf_count,
-                perf_results.get()));
-        data_algo = (perf_results.get())[best_algo_idx].algo;
-        int stride_dim = input->dims().size() - 2;
-        bool blacklist =
-            std::any_of(strides.begin(), strides.begin() + stride_dim,
-                        [=](int n) { return n != 1; });
-        if (blacklist && (static_cast<cudnnConvolutionBwdDataAlgo_t>(
-                              perf_results[best_algo_idx].algo) ==
-                              CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT_TILING ||
-                          static_cast<cudnnConvolutionBwdDataAlgo_t>(
-                              perf_results[best_algo_idx].algo) ==
-                              CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT)) {
-          data_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1;
-        }
-
-        CUDNN_ENFORCE(
-            platform::dynload::cudnnGetConvolutionBackwardDataWorkspaceSize(
-                handle, cudnn_filter_desc, cudnn_output_grad_desc,
-                cudnn_conv_desc, cudnn_input_desc, data_algo, &tmp_size));
-        auto new_workspace_size = std::max(workspace_size_in_bytes, tmp_size);
-
-        if (new_workspace_size > workspace_size_limit) {
-          VLOG(1) << "Fallback to non-v7 method to find conv algorithm becasue "
-                     "the workspace size request("
-                  << new_workspace_size << ") exceeds the limit("
-                  << workspace_size_limit << ")";
-#endif
-          CUDNN_ENFORCE(
-              platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
-                  handle, cudnn_filter_desc,
-                  // dyDesc: Handle to the previously initialized input
-                  // differential
-                  // tensor descriptor.
-                  cudnn_output_grad_desc, cudnn_conv_desc,
-                  // dxDesc: Handle to the previously initialized output tensor
-                  // descriptor.
-                  cudnn_input_desc,
-                  CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
-                  workspace_size_limit, &data_algo));
-#if CUDNN_VERSION >= 7001
-        } else {
-          workspace_size_in_bytes = new_workspace_size;
-          has_got_bwd_data_ws_size = true;
-        }
-#endif
-      }
+      // ------------------- cudnn descriptors ---------------------
+      input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
+      args1.handle = handle;
+      args1.idesc.set(*input_grad, iwo_groups);
+      args1.wdesc.set(*filter, layout_tensor, iwo_groups);
+      args1.odesc.set(*output_grad, iwo_groups);
+      args1.cdesc.set(dtype, paddings, strides, dilations, c_groups);
 
-      if (!has_got_bwd_data_ws_size) {
-        CUDNN_ENFORCE(
-            platform::dynload::cudnnGetConvolutionBackwardDataWorkspaceSize(
-                handle, cudnn_filter_desc, cudnn_output_grad_desc,
-                cudnn_conv_desc, cudnn_input_desc, data_algo, &tmp_size));
-        workspace_size_in_bytes = std::max(workspace_size_in_bytes, tmp_size);
-      }
+      using search1 = SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t>;
+      data_algo =
+          search1::Find<T>(args1, exhaustive_search, deterministic, 0, ctx);
+      workspace_size =
+          std::max(workspace_size, search1::GetWorkspaceSize(args1, data_algo));
     }
 
-    bool has_got_bwd_filter_ws_size = false;
     if (filter_grad) {
-      T* filter_grad_data = filter_grad->mutable_data<T>(ctx.GetPlace());
-      if (exhaustive_search) {
-        AlgorithmsCache<cudnnConvolutionBwdFilterAlgo_t>& f_algo_cache =
-            ctx.GetKernelConfig<
-                AlgorithmsCache<cudnnConvolutionBwdFilterAlgo_t>>(1);
-
-        filter_algo = f_algo_cache.GetAlgorithm(
-            x_dims, f_dims, strides, paddings, dilations, 0, [&]() {
-              int returned_algo_count;
-              std::array<cudnnConvolutionBwdFilterAlgoPerf_t,
-                         kNUM_CUDNN_BWD_FILTER_ALGS>
-                  filter_perf_stat;
-
-              CUDNN_ENFORCE(
-                  platform::dynload::
-                      cudnnFindConvolutionBackwardFilterAlgorithmEx(
-                          handle, cudnn_input_desc, input_data,
-                          cudnn_output_grad_desc, output_grad_data,
-                          cudnn_conv_desc, cudnn_filter_desc, filter_grad_data,
-                          kNUM_CUDNN_BWD_FILTER_ALGS, &returned_algo_count,
-                          filter_perf_stat.data(), cudnn_workspace_ptr,
-                          workspace_size_limit));
-              return filter_perf_stat[0].algo;
-            });
-        VLOG(3) << "cuDNN backward filter algo " << filter_algo;
-      } else if (FLAGS_cudnn_deterministic) {
-        filter_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1;
-      } else {
-#if CUDNN_VERSION >= 7001
-        using perf_t = cudnnConvolutionBwdFilterAlgoPerf_t;
-        int perf_count;
-        int best_algo_idx = 0;
-        std::unique_ptr<perf_t[]> perf_results(
-            new perf_t[kNUM_CUDNN_BWD_FILTER_ALGS]);
-
-        CUDNN_ENFORCE(
-            platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm_v7(
-                handle, cudnn_input_desc, cudnn_output_grad_desc,
-                cudnn_conv_desc, cudnn_filter_desc, kNUM_CUDNN_BWD_FILTER_ALGS,
-                &perf_count, perf_results.get()));
-        filter_algo = (perf_results.get())[best_algo_idx].algo;
-
-        CUDNN_ENFORCE(
-            platform::dynload::cudnnGetConvolutionBackwardFilterWorkspaceSize(
-                handle, cudnn_input_desc, cudnn_output_grad_desc,
-                cudnn_conv_desc, cudnn_filter_desc, filter_algo, &tmp_size));
-        auto new_workspace_size = std::max(workspace_size_in_bytes, tmp_size);
-
-        if (new_workspace_size > workspace_size_limit) {
-          VLOG(1) << "Fallback to non-v7 method to find conv algorithm becasue "
-                     "the workspace size request("
-                  << new_workspace_size << ") exceeds the limit("
-                  << workspace_size_limit << ")";
-#endif
-          CUDNN_ENFORCE(
-              platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
-                  handle, cudnn_input_desc, cudnn_output_grad_desc,
-                  cudnn_conv_desc, cudnn_filter_desc,
-                  CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
-                  workspace_size_limit, &filter_algo));
-#if CUDNN_VERSION >= 7001
-        } else {
-          workspace_size_in_bytes = new_workspace_size;
-          has_got_bwd_filter_ws_size = true;
-        }
-#endif
-      }
-
-      if (!has_got_bwd_filter_ws_size) {
-        CUDNN_ENFORCE(
-            platform::dynload::cudnnGetConvolutionBackwardFilterWorkspaceSize(
-                handle, cudnn_input_desc, cudnn_output_grad_desc,
-                cudnn_conv_desc, cudnn_filter_desc, filter_algo, &tmp_size));
-        workspace_size_in_bytes = std::max(workspace_size_in_bytes, tmp_size);
-      }
-    }
-
-    PADDLE_ENFORCE_LE(workspace_size_in_bytes, workspace_size_limit,
-                      "workspace_size to be allocated exceeds the limit");
-
-    // ------------------- cudnn conv workspace ---------------------
-    if (!cudnn_workspace_ptr) {
-      cudnn_workspace =
-          ctx.AllocateTmpTensor<int8_t, platform::CUDADeviceContext>(
-              framework::make_ddim(
-                  {static_cast<int64_t>(workspace_size_in_bytes)}),
-              dev_ctx);
-      cudnn_workspace_ptr = static_cast<void*>(cudnn_workspace.data<int8_t>());
-      VLOG(2) << "Cudnn workspace size bwd: "
-              << static_cast<double>(workspace_size_in_bytes) / (1 << 20)
-              << " MB";
+      // ------------------- cudnn descriptors ---------------------
+      filter_grad_data = filter_grad->mutable_data<T>(ctx.GetPlace());
+      args2.handle = handle;
+      args2.idesc.set(*input, iwo_groups);
+      args2.wdesc.set(*filter_grad, layout_tensor, iwo_groups);
+      args2.odesc.set(*output_grad, iwo_groups);
+      args2.cdesc.set(dtype, paddings, strides, dilations, c_groups);
+
+      using search2 = SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t>;
+      filter_algo =
+          search2::Find<T>(args2, exhaustive_search, deterministic, 1, ctx);
+      workspace_size = std::max(workspace_size,
+                                search2::GetWorkspaceSize(args2, filter_algo));
     }
 
     // ------------------- cudnn conv backward data ---------------------
     ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
     if (input_grad) {
-      T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
       // Because beta is zero, it is unnecessary to reset input_grad.
-
       for (int i = 0; i < groups; i++) {
-        CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBackwardData(
-            handle, &alpha, cudnn_filter_desc,
-            filter_data + i * group_offset_filter, cudnn_output_grad_desc,
-            output_grad_data + i * group_offset_out, cudnn_conv_desc, data_algo,
-            cudnn_workspace_ptr, workspace_size_in_bytes, &beta,
-            cudnn_input_desc, input_grad_data + i * group_offset_in));
+        workspace_handle.RunFunc(
+            [&](void* cudnn_workspace_ptr) {
+              CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBackwardData(
+                  handle, &alpha, args1.wdesc.desc(),
+                  filter_data + i * group_offset_filter, args1.odesc.desc(),
+                  output_grad_data + i * group_offset_out, args1.cdesc.desc(),
+                  data_algo, cudnn_workspace_ptr, workspace_size, &beta,
+                  args1.idesc.desc(), input_grad_data + i * group_offset_in));
+            },
+            workspace_size);
       }
     }
     // ------------------- cudnn conv backward filter ---------------------
     if (filter_grad) {
-      T* filter_grad_data = filter_grad->mutable_data<T>(ctx.GetPlace());
       // Because beta is zero, it is unnecessary to reset filter_grad.
       for (int i = 0; i < groups; i++) {
-        CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter(
-            handle, &alpha, cudnn_input_desc, input_data + i * group_offset_in,
-            cudnn_output_grad_desc, output_grad_data + i * group_offset_out,
-            cudnn_conv_desc, filter_algo, cudnn_workspace_ptr,
-            workspace_size_in_bytes, &beta, cudnn_filter_desc,
-            filter_grad_data + i * group_offset_filter));
+        workspace_handle.RunFunc(
+            [&](void* cudnn_workspace_ptr) {
+              CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter(
+                  handle, &alpha, args2.idesc.desc(),
+                  input_data + i * group_offset_in, args2.odesc.desc(),
+                  output_grad_data + i * group_offset_out, args2.cdesc.desc(),
+                  filter_algo, cudnn_workspace_ptr, workspace_size, &beta,
+                  args2.wdesc.desc(),
+                  filter_grad_data + i * group_offset_filter));
+            },
+            workspace_size);
       }
     }
   }