refactor(dnn): make cudnn_frontend work

GitOrigin-RevId: f089f93494

dnn/CMakeLists.txt

@@ -54,7 +54,10 @@ if(MGE_WITH_CUDA)
   add_library(cutlass INTERFACE)
   target_include_directories(
     cutlass
-    INTERFACE $<BUILD_INTERFACE:${PROJECT_SOURCE_DIR}/third_party/cutlass/include>)
+    INTERFACE
+      $<BUILD_INTERFACE:${PROJECT_SOURCE_DIR}/third_party/cutlass/include>
+      $<BUILD_INTERFACE:${PROJECT_SOURCE_DIR}/third_party/cutlass/tools/util/include>)
+
   add_library(cudnn-frontend INTERFACE)
   target_include_directories(
     cudnn-frontend

dnn/include/megdnn/algorithm_cache.h

@@ -31,7 +31,7 @@ public:
         }
     };
 
-    class Key {
+    struct Key {
         Handle* m_handle;
         uint32_t m_opr_type;
         const TensorLayout* m_inp_layouts_ptr;

dnn/src/cuda/conv_bias/algo.cpp

@@ -15,7 +15,7 @@ ConvBiasForwardImpl::AlgoPack::AlgoPack() {
     non_cudnn_algos.push_back(&batched_matmul);
     non_cudnn_algos.push_back(&int1_simple);
 
-#if CUDNN_VERSION > 8004
+#if CUDNN_VERSION >= 8020
     all_algos.push_back(&cudnn_conv_v8);
     all_algos.push_back(&cudnn_conv_bias_activation_v8);
 #endif

dnn/src/cuda/conv_bias/algo.h

@@ -173,10 +173,10 @@ public:
 
     bool is_cudnn() const override { return true; }
 
-    size_t get_preprocess_workspace_in_bytes(const SizeArgs& args) const override;
-    SmallVector<TensorLayout> deduce_preprocessed_filter_layout(
-            const SizeArgs& args) const override;
-    void exec_preprocess(const ExecArgs& args) const override;
+    // size_t get_preprocess_workspace_in_bytes(const SizeArgs& args) const override;
+    // SmallVector<TensorLayout> deduce_preprocessed_filter_layout(
+    //         const SizeArgs& args) const override;
+    // void exec_preprocess(const ExecArgs& args) const override;
 
 protected:
     virtual size_t cudnn_get_workspace_in_bytes(const SizeArgs& args) const = 0;
@@ -237,7 +237,7 @@ private:
     CudnnAlgoPack::Attr m_attr;
 };
 
-#if CUDNN_VERSION > 8004
+#if CUDNN_VERSION >= 8020
 class ConvBiasForwardImpl::AlgoCUDNNConvBiasActivationV8 final
         : public AlgoCUDNNConvBiasActivationBase {
 public:
@@ -414,7 +414,7 @@ private:
     CudnnAlgoPack::Attr m_attr;
 };
 
-#if CUDNN_VERSION > 8004
+#if CUDNN_VERSION >= 8020
 class ConvBiasForwardImpl::AlgoCUDNNConvV8 final : public AlgoCUDNNConvBase {
 public:
     AlgoCUDNNConvV8() : AlgoCUDNNConvBase() {
@@ -1247,7 +1247,7 @@ public:
     AlgoGroupConvGeneral group;
     AlgoBFloat16 bfloat16;
     AlgoSimpleInt1 int1_simple;
-#if CUDNN_VERSION > 8004
+#if CUDNN_VERSION >= 8020
     AlgoCUDNNConvV8 cudnn_conv_v8;
     AlgoCUDNNConvBiasActivationV8 cudnn_conv_bias_activation_v8;
 #endif

dnn/src/cuda/conv_bias/cudnn_conv_base.cpp

@@ -1,14 +1,3 @@
-/**
- * \file dnn/src/cuda/conv_bias/cudnn_conv_base.cpp
- * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
- *
- * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- */
-
 #include "src/common/conv_bias.h"
 #include "src/cuda/conv_bias/algo.h"
 #include "src/cuda/utils.h"

dnn/src/cuda/conv_bias/cudnn_conv_bias_activation_base.cpp

@@ -1,14 +1,3 @@
-/**
- * \file dnn/src/cuda/conv_bias/cudnn_conv_bias_activation_base.cpp
- * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
- *
- * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- */
-
 #include "megdnn/oprs/general.h"
 
 #include "./algo.h"
@@ -26,19 +15,21 @@ size_t ConvBiasForwardImpl::AlgoCUDNNConvBiasActivationBase::get_workspace_in_by
         const SizeArgs& args) const {
     auto workspace_size = cudnn_get_workspace_in_bytes(args);
 
-    auto&& param = args.opr->param();
-    if (args.preprocessed_filter == nullptr) {
-        if (args.bias_layout && args.bias_layout->dtype != dtype::Float32() &&
-            args.src_layout->dtype.category() != DTypeCategory::FLOAT) {
-            // cudnn require bias to be float when executing CONFIG_INT
-            // convert bias to float if bias is not float at first
-            workspace_size += sizeof(float) * args.bias_layout->span().dist_elem();
-        }
-        if (param.format == param::ConvBias::Format::NCHW32) {
-            workspace_size += args.filter_layout->span().dist_byte() +
-                              args.bias_layout->span().dist_byte();
-        }
+    // if (args.preprocessed_filter == nullptr) {
+    if (args.bias_layout && args.bias_layout->dtype != dtype::Float32() &&
+        args.src_layout->dtype.category() != DTypeCategory::FLOAT) {
+        // cudnn require bias to be float when executing CONFIG_INT
+        // convert bias to float if bias is not float at first
+        workspace_size += sizeof(float) * args.bias_layout->span().dist_elem();
     }
+    // #if CUDNN_VERSION >= 7500
+    //         auto&& param = args.opr->param();
+    //         if (param.format == param::ConvBias::Format::NCHW32) {
+    //             workspace_size += args.filter_layout->span().dist_byte() +
+    //                               args.bias_layout->span().dist_byte();
+    //         }
+    // #endif
+    // }
     return workspace_size;
 }
 
@@ -56,55 +47,62 @@ void ConvBiasForwardImpl::AlgoCUDNNConvBiasActivationBase::exec(
     TensorND filter_tensor;
     TensorND bias_tensor;
 
-    auto&& param = args.opr->param();
-    if (args.preprocessed_filter != nullptr) {
-        bias_tensor = TensorND{
-                args.bias_tensor->layout,
-                args.preprocessed_filter->tensors[0].raw_ptr()};
-        if (param.format == Param::Format::NCHW32) {
-            megdnn_assert(args.preprocessed_filter->tensors.size() == 2);
-            filter_tensor = TensorND{
-                    args.filter_tensor->layout,
-                    args.preprocessed_filter->tensors[1].raw_ptr()};
-        } else {
-            filter_tensor = *args.filter_tensor;
-        }
-    } else {
-        if (args.bias_layout && args.bias_layout->dtype != dtype::Float32() &&
-            args.src_layout->dtype.category() != DTypeCategory::FLOAT) {
-            auto cvt = args.handle->create_operator<TypeCvt>();
-            auto float_bias_layout = *args.bias_layout;
-            auto converted_bias_layout = *args.bias_layout;
-            converted_bias_layout.dtype = dtype::QuantizedS32(alpha);
-            float_bias_layout.dtype = dtype::Float32();
-            auto bias_size_in_bytes = float_bias_layout.span().dist_byte();
-            megdnn_assert(args.workspace.size >= bias_size_in_bytes);
-            cvt->exec(
-                    {args.bias_tensor->raw_ptr(), converted_bias_layout},
-                    TensorND{workspace_ptr, float_bias_layout});
-
-            bias_ptr = workspace_ptr;
-            workspace_ptr += bias_size_in_bytes;
-            workspace_size -= bias_size_in_bytes;
-        }
-        if (param.format == Param::Format::NCHW32) {
-            size_t reorder_workspace_size =
-                    args.filter_tensor->layout.span().dist_byte() +
-                    args.bias_tensor->layout.span().dist_byte();
-            auto reorder_filter_ptr = workspace_ptr;
-            auto reorder_bias_ptr =
-                    workspace_ptr + args.filter_tensor->layout.span().dist_byte();
-            cudnn_reorder_filer_and_bias_nchw32(
-                    cudnn_handle(args.opr->handle()), args.filter_tensor->raw_ptr(),
-                    args.filter_meta, bias_ptr, reorder_filter_ptr, reorder_bias_ptr);
-            filter_tensor = TensorND(args.filter_tensor->layout, reorder_filter_ptr);
-            bias_ptr = reorder_bias_ptr;
-            workspace_ptr += reorder_workspace_size;
-            workspace_size -= reorder_workspace_size;
-        } else {
-            filter_tensor = *args.filter_tensor;
-        }
+    // if (args.preprocessed_filter != nullptr) {
+    //     bias_tensor = TensorND{
+    //             args.bias_tensor->layout,
+    //             args.preprocessed_filter->tensors[0].raw_ptr()};
+    //     // #if CUDNN_VERSION >= 7500
+    //     //         auto&& param = args.opr->param();
+    //     //         if (param.format == Param::Format::NCHW32) {
+    //     //             megdnn_assert(args.preprocessed_filter->tensors.size() == 2);
+    //     //             filter_tensor = TensorND{
+    //     //                     args.filter_tensor->layout,
+    //     //                     args.preprocessed_filter->tensors[1].raw_ptr()};
+    //     //         }
+    //     // #else
+    //     filter_tensor = *args.filter_tensor;
+    //     // #endif
+    // } else {
+    if (args.bias_layout && args.bias_layout->dtype != dtype::Float32() &&
+        args.src_layout->dtype.category() != DTypeCategory::FLOAT) {
+        auto cvt = args.handle->create_operator<TypeCvt>();
+        auto float_bias_layout = *args.bias_layout;
+        auto converted_bias_layout = *args.bias_layout;
+        converted_bias_layout.dtype = dtype::QuantizedS32(alpha);
+        float_bias_layout.dtype = dtype::Float32();
+        auto bias_size_in_bytes = float_bias_layout.span().dist_byte();
+        megdnn_assert(args.workspace.size >= bias_size_in_bytes);
+        cvt->exec(
+                {args.bias_tensor->raw_ptr(), converted_bias_layout},
+                TensorND{workspace_ptr, float_bias_layout});
+
+        bias_ptr = workspace_ptr;
+        workspace_ptr += bias_size_in_bytes;
+        workspace_size -= bias_size_in_bytes;
     }
+    // #if CUDNN_VERSION >= 7500
+    //         auto&& param = args.opr->param();
+    //         if (param.format == Param::Format::NCHW32) {
+    //             size_t reorder_workspace_size =
+    //                     args.filter_tensor->layout.span().dist_byte() +
+    //                     args.bias_tensor->layout.span().dist_byte();
+    //             auto reorder_filter_ptr = workspace_ptr;
+    //             auto reorder_bias_ptr =
+    //                     workspace_ptr +
+    //                     args.filter_tensor->layout.span().dist_byte();
+    //             cudnn_reorder_filter_and_bias_nchw32(
+    //                     cudnn_handle(args.opr->handle()),
+    //                     args.filter_tensor->raw_ptr(), args.filter_meta,
+    //                     bias_ptr, reorder_filter_ptr, reorder_bias_ptr);
+    //             filter_tensor = TensorND(args.filter_tensor->layout,
+    //             reorder_filter_ptr); bias_ptr = reorder_bias_ptr; workspace_ptr
+    //             += reorder_workspace_size; workspace_size -=
+    //             reorder_workspace_size;
+    //         }
+    // #else
+    filter_tensor = *args.filter_tensor;
+    // #endif
+    // }
 
     bias_tensor = TensorND{args.bias_tensor->layout, bias_ptr};
     ExecArgs exec_args{
@@ -153,58 +151,64 @@ void ConvBiasForwardImpl::AlgoCUDNNConvBiasActivationBase::exec(
     }
 }
 
-size_t ConvBiasForwardImpl::AlgoCUDNNConvBiasActivationBase::
-        get_preprocess_workspace_in_bytes(const SizeArgs& args) const {
-    auto&& param = args.opr->param();
-    if (param.format == Param::Format::NCHW32) {
-        return args.bias_layout->span().dist_byte();
-    }
-    return 0_z;
-}
-
-SmallVector<TensorLayout> ConvBiasForwardImpl::AlgoCUDNNConvBiasActivationBase::
-        deduce_preprocessed_filter_layout(const SizeArgs& args) const {
-    auto&& param = args.opr->param();
-    if (param.format == Param::Format::NCHW32) {
-        return {args.bias_layout->collapse_contiguous(),
-                args.filter_layout->collapse_contiguous()};
-    } else {
-        return {args.bias_layout->collapse_contiguous()};
-    }
-}
-
-void ConvBiasForwardImpl::AlgoCUDNNConvBiasActivationBase::exec_preprocess(
-        const ExecArgs& args) const {
-    float alpha, beta;
-    std::tie(alpha, beta) = cudnn_get_conv_bias_act_scale_param(
-            args.src_tensor->layout, args.dst_tensor->layout,
-            args.filter_tensor->layout, args.bias_tensor->layout,
-            args.z_tensor->layout);
-    MEGDNN_MARK_USED_VAR(beta);
-
-    auto workspace_ptr = args.workspace.raw_ptr;
-    auto workspace_size = args.workspace.size;
-    auto bias_ptr = workspace_size > 0 ? workspace_ptr
-                                       : args.preprocessed_filter->tensors[0].raw_ptr();
-    if (args.bias_layout && args.bias_layout->dtype != dtype::Float32() &&
-        args.src_layout->dtype.category() != DTypeCategory::FLOAT) {
-        auto cvt = args.handle->create_operator<TypeCvt>();
-        auto float_bias_layout = *args.bias_layout;
-        auto converted_bias_layout = *args.bias_layout;
-        converted_bias_layout.dtype = dtype::QuantizedS32(alpha);
-        float_bias_layout.dtype = dtype::Float32();
-
-        cvt->exec(
-                {args.bias_tensor->raw_ptr(), converted_bias_layout},
-                TensorND{bias_ptr, float_bias_layout});
-    }
-    if (args.opr->param().format == Param::Format::NCHW32) {
-        auto reorder_filter_ptr = args.preprocessed_filter->tensors[1].raw_ptr();
-        auto reorder_bias_ptr = args.preprocessed_filter->tensors[0].raw_ptr();
-        cudnn_reorder_filer_and_bias_nchw32(
-                cudnn_handle(args.opr->handle()), args.filter_tensor->raw_ptr(),
-                args.filter_meta, bias_ptr, reorder_filter_ptr, reorder_bias_ptr);
-    }
-}
+// size_t ConvBiasForwardImpl::AlgoCUDNNConvBiasActivationBase::
+//         get_preprocess_workspace_in_bytes(const SizeArgs&) const {
+// #if CUDNN_VERSION >= 7500
+//     auto&& param = args.opr->param();
+//     if (param.format == Param::Format::NCHW32) {
+//         return args.bias_layout->span().dist_byte();
+//     }
+// #endif
+//     return 0_z;
+// }
+
+// SmallVector<TensorLayout> ConvBiasForwardImpl::AlgoCUDNNConvBiasActivationBase::
+//         deduce_preprocessed_filter_layout(const SizeArgs& args) const {
+// #if CUDNN_VERSION >= 7500
+//     auto&& param = args.opr->param();
+//     if (param.format == Param::Format::NCHW32) {
+//         return {args.bias_layout->collapse_contiguous(),
+//                 args.filter_layout->collapse_contiguous()};
+//     }
+// #endif
+//     return {args.bias_layout->collapse_contiguous()};
+// }
+
+// void ConvBiasForwardImpl::AlgoCUDNNConvBiasActivationBase::exec_preprocess(
+//         const ExecArgs& args) const {
+//     float alpha, beta;
+//     std::tie(alpha, beta) = cudnn_get_conv_bias_act_scale_param(
+//             args.src_tensor->layout, args.dst_tensor->layout,
+//             args.filter_tensor->layout, args.bias_tensor->layout,
+//             args.z_tensor->layout);
+//     MEGDNN_MARK_USED_VAR(beta);
+
+//     auto workspace_ptr = args.workspace.raw_ptr;
+//     auto workspace_size = args.workspace.size;
+//     auto bias_ptr = workspace_size > 0 ? workspace_ptr
+//                                        :
+//                                        args.preprocessed_filter->tensors[0].raw_ptr();
+//     if (args.bias_layout && args.bias_layout->dtype != dtype::Float32() &&
+//         args.src_layout->dtype.category() != DTypeCategory::FLOAT) {
+//         auto cvt = args.handle->create_operator<TypeCvt>();
+//         auto float_bias_layout = *args.bias_layout;
+//         auto converted_bias_layout = *args.bias_layout;
+//         converted_bias_layout.dtype = dtype::QuantizedS32(alpha);
+//         float_bias_layout.dtype = dtype::Float32();
+
+//         cvt->exec(
+//                 {args.bias_tensor->raw_ptr(), converted_bias_layout},
+//                 TensorND{bias_ptr, float_bias_layout});
+//     }
+// #if CUDNN_VERSION >= 7500
+//     if (args.opr->param().format == Param::Format::NCHW32) {
+//         auto reorder_filter_ptr = args.preprocessed_filter->tensors[1].raw_ptr();
+//         auto reorder_bias_ptr = args.preprocessed_filter->tensors[0].raw_ptr();
+//         cudnn_reorder_filter_and_bias_nchw32(
+//                 cudnn_handle(args.opr->handle()), args.filter_tensor->raw_ptr(),
+//                 args.filter_meta, bias_ptr, reorder_filter_ptr, reorder_bias_ptr);
+//     }
+// #endif
+// }
 
 // vim: syntax=cpp.doxygen

dnn/src/cuda/conv_bias/cudnn_conv_bias_activation_v8.cpp

@@ -1,14 +1,3 @@
-/**
- * \file dnn/src/cuda/conv_bias/cudnn_conv_bias_activation_v8.cpp
- * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
- *
- * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- */
-
 #include "megdnn/oprs/general.h"
 
 #include "./algo.h"
@@ -17,7 +6,7 @@
 #include "src/cuda/cudnn_wrapper_v8.h"
 #include "src/cuda/utils.h"
 
-#if CUDNN_VERSION >= 8004
+#if CUDNN_VERSION >= 8020
 using namespace megdnn;
 using namespace cuda;
 using namespace conv_bias;

dnn/src/cuda/conv_bias/cudnn_conv_v8.cpp

@@ -1,20 +1,9 @@
-/**
- * \file dnn/src/cuda/conv_bias/cudnn_conv_v8.cpp
- * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
- *
- * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- */
-
 #include "src/common/conv_bias.h"
 #include "src/cuda/conv_bias/algo.h"
 #include "src/cuda/cudnn_wrapper_v8.h"
 #include "src/cuda/utils.h"
 
-#if CUDNN_VERSION >= 8004
+#if CUDNN_VERSION >= 8020
 using namespace megdnn;
 using namespace cuda;
 using namespace conv_bias;

dnn/src/cuda/conv_bias/helper.cpp

@@ -239,7 +239,8 @@ std::pair<float, float> cudnn_get_conv_bias_act_scale_param(
     return {alpha, beta};
 }
 
-void cudnn_reorder_filer_and_bias_nchw32(
+#if CUDNN_VERSION >= 7500
+void cudnn_reorder_filter_and_bias_nchw32(
         const cudnnHandle_t& handle, const void* filter_ptr,
         const CanonizedFilterMeta& fm, const void* bias_ptr, void* reordered_filter_ptr,
         void* reordered_bias_ptr) {
@@ -250,6 +251,8 @@ void cudnn_reorder_filer_and_bias_nchw32(
             handle, filter_desc.desc, CUDNN_DEFAULT_REORDER, filter_ptr,
             reordered_filter_ptr, reorder_bias, bias_ptr, reordered_bias_ptr));
 }
+#endif
+
 }  // namespace conv_bias
 }  // namespace cuda
 }  // namespace megdnn

dnn/src/cuda/conv_bias/helper.h

@@ -117,11 +117,12 @@ std::pair<float, float> cudnn_get_conv_bias_act_scale_param(
         const TensorLayout& x, const TensorLayout& y, const TensorLayout& w,
         const TensorLayout& b, const TensorLayout& z);
 
-void cudnn_reorder_filer_and_bias_nchw32(
+#if CUDNN_VERSION >= 7500
+void cudnn_reorder_filter_and_bias_nchw32(
         const cudnnHandle_t& handle, const void* filter_ptr,
         const CanonizedFilterMeta& fm, const void* bias_ptr, void* reordered_filter_ptr,
         void* reordered_bias_ptr);
-
+#endif
 }  // namespace conv_bias
 }  // namespace cuda
 }  // namespace megdnn

dnn/src/cuda/conv_bias/opr_impl.cpp

@@ -47,7 +47,7 @@ ConvBiasForward::Algorithm* ConvBiasForwardImpl::get_algorithm_heuristic(
         const AlgoAttribute& positive_attr, const AlgoAttribute& negative_attr) {
     using namespace conv_bias;
     AlgoBase::SizeArgs args{this, src, filter, bias, z, dst};
-#if CUDNN_VERSION >= 8004
+#if CUDNN_VERSION >= 8020
     if (sm_algo_pack.cudnn_conv_v8.is_available_attribute(
                 args, positive_attr, negative_attr, workspace_limit_in_bytes)) {
         return &sm_algo_pack.cudnn_conv_v8;

dnn/src/cuda/conv_bias/opr_impl.h

@@ -32,12 +32,10 @@ public:
     const char* get_algorithm_set_name() const override;
 
     class AlgoBase;
-    class AlgoCUDNNConvBiasActivation;
     class AlgoChanwise;
     class AlgoChanwiseSmall;
     class AlgoDepthwiseLargeFilter;
     class AlgoChanwise8x8x32;
-    class AlgoCUDNNConv;
     class AlgoFallbackNCHWQS8;
     class AlgoInplaceMatmul;
     class AlgoMatmul;
@@ -67,8 +65,10 @@ public:
     class AlgoFloat32NCHWFMAImplicitBatchedGemm;
     class AlgoFloat16NCHWHMMAImplicitBatchedGemm;
     class AlgoCUDNNConvBase;
+    class AlgoCUDNNConv;
     class AlgoCUDNNConvBiasActivationBase;
-#if CUDNN_VERSION > 8004
+    class AlgoCUDNNConvBiasActivation;
+#if CUDNN_VERSION >= 8020
     class AlgoCUDNNConvV8;
     class AlgoCUDNNConvBiasActivationV8;
 #endif

dnn/src/cuda/cudnn_wrapper_v8.cpp

@@ -1,13 +1,4 @@
-/**
- * \file dnn/src/cuda/cudnn_wrapper_v8.cpp
- * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
- *
- * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- */
+#if CUDNN_VERSION >= 8020
 
 #include "src/cuda/cudnn_wrapper_v8.h"
 #include "src/cuda/cudnn_wrapper.h"
@@ -19,7 +10,7 @@
 
 #include "cudnn_frontend_EngineConfigGenerator.h"
 
-#include "megdnn/heuristic_cache.h"
+#include "megdnn/algorithm_cache.h"
 
 using namespace megdnn;
 using namespace cuda;
@@ -240,9 +231,9 @@ auto make_activation_descriptor(
 
 // high-level api for convolution execution
 struct StaticData {
-    using Key = megdnn::HeuristicCache::Key;
-    using KeyStorage = megdnn::HeuristicCache::KeyStorage;
-    using KeyHash = megdnn::HeuristicCache::Hash;
+    using Key = megdnn::AlgorithmCache::Key;
+    using KeyStorage = megdnn::AlgorithmCache::KeyStorage;
+    using KeyHash = megdnn::AlgorithmCache::Hash;
     using Result = cudnn_frontend::ExecutionPlan;
     using CudnnFrontendExecutionPlanCache =
             std::unordered_map<KeyStorage, Result, KeyHash>;
@@ -682,4 +673,5 @@ void megdnn::cuda::run_conv_bias_act_with_plan(
             handle, plan.get_raw_desc(), variant_pack.get_raw_desc()));
 }
 
+#endif
 // vim: syntax=cpp.doxygen

dnn/src/cuda/cudnn_wrapper_v8.h

@@ -1,15 +1,6 @@
-/**
- * \file dnn/src/cuda/cudnn_wrapper_v8.h
- * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
- *
- * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- */
 #pragma once
 
+#if CUDNN_VERSION >= 8020
 #include "megdnn/basic_types.h"
 #include "megdnn/oprs/nn.h"
 #include "src/common/utils.h"
@@ -67,4 +58,5 @@ void run_conv_bias_act_with_plan(
 }  // namespace cuda
 }  // namespace megdnn
 
+#endif
 // vim: syntax=cpp.doxygen

dnn/src/cuda/handle.cpp

@@ -58,11 +58,6 @@ HandleImpl::HandleImpl(megcoreComputingHandle_t comp_handle)
             For example `export CUDA_CACHE_MAXSIZE=2147483647` and `export CUDA_CACHE_PATH=/data/.cuda_cache`)");
     }
 #endif
-    size_t free, tot;
-    cudaMemGetInfo(&free, &tot);
-    printf("before cudnn create, free: %.2f MB, tot: %.2f MB, allocated: %.2f MB\n",
-           free / 1024.0 / 1024.0, tot / 1024.0 / 1024.0,
-           (tot - free) / 1024.0 / 1024.0);
     cudnn_check(cudnnCreate(&m_cudnn_handle));
     cublas_check(cublasCreate(&m_cublas_handle));
 #if CUDA_VERSION >= 10010
@@ -74,11 +69,6 @@ HandleImpl::HandleImpl(megcoreComputingHandle_t comp_handle)
     cudnn_check(cudnnSetStream(m_cudnn_handle, stream()));
     cublas_check(cublasSetStream(m_cublas_handle, stream()));
 
-#if CUDNN_VERSION >= 8004
-//    cudnn_check(cudnnOpsInferVersionCheck());
-//    cudnn_check(cudnnCnnInferVersionCheck());
-#endif
-
     // Note that all cublas scalars (alpha, beta) and scalar results such as dot
     // output resides at device side.
     cublas_check(cublasSetPointerMode(m_cublas_handle, CUBLAS_POINTER_MODE_DEVICE));
@@ -92,11 +82,6 @@ HandleImpl::HandleImpl(megcoreComputingHandle_t comp_handle)
             cudaMemcpyHostToDevice, stream()));
     cuda_check(cudaStreamSynchronize(stream()));
 
-    cudaMemGetInfo(&free, &tot);
-    printf("after cudnn create, free: %.2f MB, tot: %.2f MB, allocated: %.2f MB\n",
-           free / 1024.0 / 1024.0, tot / 1024.0 / 1024.0,
-           (tot - free) / 1024.0 / 1024.0);
-
     // check tk1
     m_is_tegra_k1 = (strcmp(m_device_prop->name, "GK20A") == 0);
     m_cusolver_handle = nullptr;

dnn/test/cuda/conv_v8.cpp

@@ -1,13 +1,3 @@
-/**
- * \file dnn/test/cuda/conv_bias.cpp
- * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
- *
- * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- */
 #include "megdnn/dtype.h"
 #include "test/cuda/fixture.h"
 
@@ -26,7 +16,7 @@ using namespace megdnn;
 using namespace test;
 using namespace conv_bias;
 
-#if CUDNN_VERSION >= 8004
+#if CUDNN_VERSION >= 8020
 TEST_F(CUDA, CONV_V8_FLOAT) {
     Checker<ConvBiasForward> checker(handle_cuda());
     checker.set_before_exec_callback(