feat(dnn): add deterministic max pooling

GitOrigin-RevId: 9ab4c7a74839b661fe5ed052a20a66b6732c09d0

dnn/include/megdnn/oprs.h

@@ -48,6 +48,8 @@ INST_ARITY(megdnn::ConvBias, 4, 1);
 INST_ARITY(megdnn::DeformableConvBackwardData, 5, 3);
 INST_ARITY(megdnn::MatrixMul, 2, 1);
 INST_ARITY(megdnn::BatchedMatrixMul, 2, 1);
+INST_ARITY(megdnn::PoolingForward, 1, 1);
+INST_ARITY(megdnn::PoolingBackward, 3, 1);
 
 #undef INST_ARITY
 

dnn/include/megdnn/oprs/base.h

@@ -259,6 +259,8 @@ public:
         DEFORMABLE_CONV_BACKWARD_FILTER,
         CONVBIAS_FORWARD,
         BATCH_CONV_FORWARD,
+        POOLING_FORWARD,
+        POOLING_BACKWARD,
     };
 
     struct SearchItem {
@@ -334,6 +336,63 @@ private:
     ExecutionPolicy m_execution_policy;
 };
 
+//! specialize for nargs == 2
+template <class Opr>
+class MultiAlgoOpr<Opr, 2> : public MultiAlgoOpr<Opr, -1> {
+public:
+    using Algorithm = detail::Algorithm;
+    using AlgorithmInfo = detail::Algorithm::Info;
+    using AlgoAttribute = detail::Algorithm::Attribute;
+
+    //! get all possible algorithm decriptions for the specified layouts
+    std::vector<AlgorithmInfo> get_all_algorithms_info(const TensorLayout& p0,
+                                                       const TensorLayout& p1) {
+        std::vector<AlgorithmInfo> ret;
+        for (auto&& algo : get_all_algorithms(p0, p1)) {
+            ret.emplace_back(algo->info());
+        }
+        return ret;
+    }
+
+    /**
+     * \brief Returns the best algorithm information which indicate the
+     * algorithm by heuristic.
+     *
+     * The selected algorithm should not use workspace more than
+     * \p workspace_limit_in_bytes.
+     */
+    AlgorithmInfo get_algorithm_info_heuristic(
+            const TensorLayout& p0, const TensorLayout& p1,
+            size_t workspace_limit_in_bytes =
+                    std::numeric_limits<size_t>::max(),
+            const AlgoAttribute& positive_attr = AlgoAttribute::DEFAULT,
+            const AlgoAttribute& negative_attr = AlgoAttribute::DEFAULT) {
+        return get_algorithm_heuristic(p0, p1, workspace_limit_in_bytes,
+                                       positive_attr, negative_attr)
+                ->info();
+    }
+
+protected:
+    ~MultiAlgoOpr() = default;
+
+    //! get all possible algorithms for the specified layouts
+    virtual std::vector<Algorithm*> get_all_algorithms(
+            const TensorLayout& p0, const TensorLayout& p1) = 0;
+
+    /**
+     * \brief Returns the best algorithm by heuristic.
+     *
+     * The selected algorithm should not use workspace more than
+     * \p workspace_limit_in_bytes.
+     */
+    virtual Algorithm* get_algorithm_heuristic(
+            const TensorLayout& p0, const TensorLayout& p1,
+            size_t workspace_limit_in_bytes =
+                    std::numeric_limits<size_t>::max(),
+            const AlgoAttribute& positive_attr = AlgoAttribute::DEFAULT,
+            const AlgoAttribute& negative_attr = AlgoAttribute::DEFAULT) = 0;
+};
+
 //! specialize for nargs == 3
 template <class Opr>
 class MultiAlgoOpr<Opr, 3> : public MultiAlgoOpr<Opr, -1> {

dnn/include/megdnn/oprs/nn.h

@@ -713,7 +713,8 @@ protected:
     void check_layout_fwd(const TensorLayout& src, const TensorLayout& dst);
 };
 
-class PoolingForward : public PoolingBase {
+class PoolingForward : public PoolingBase,
+                       public detail::MultiAlgoOpr<PoolingForward, 2> {
     DEF_OPR_IMPL(PoolingForward, PoolingBase, 1, 1);
 
 public:
@@ -734,7 +735,8 @@ protected:
 
 using Pooling = PoolingForward;
 
-class PoolingBackward : public PoolingBase {
+class PoolingBackward : public PoolingBase,
+                        public detail::MultiAlgoOpr<PoolingBackward, 4> {
     DEF_OPR_IMPL(PoolingBackward, PoolingBase, 3, 1);
 
 public:

dnn/src/common/algo_chooser.h

@@ -69,7 +69,7 @@ std::vector<typename Opr::Algorithm*> get_all_algorithms(
             ret.push_back(i);
         }
     }
-    megdnn_assert(!ret.empty(), "no conv algorithm for %s",
+    megdnn_assert(!ret.empty(), "no algorithm for %s",
                   args.to_string().c_str());
     return ret;
 }

dnn/src/cuda/cudnn_wrapper.cpp

@@ -294,32 +294,6 @@ void ConvDesc::set(DType data_type, const param::Convolution& param,
 #endif
 }
 
-PoolingDesc::PoolingDesc() {
-    cudnn_check(cudnnCreatePoolingDescriptor(&desc));
-}
-
-PoolingDesc::~PoolingDesc() {
-    cudnn_check(cudnnDestroyPoolingDescriptor(desc));
-}
-
-void PoolingDesc::set(const param::Pooling& param) {
-    cudnnPoolingMode_t mode;
-    switch (param.mode) {
-        case param::Pooling::Mode::MAX:
-            mode = CUDNN_POOLING_MAX;
-            break;
-        case param::Pooling::Mode::AVERAGE:
-            mode = CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
-            break;
-        case param::Pooling::Mode::AVERAGE_COUNT_EXCLUDE_PADDING:
-            mode = CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING;
-            break;
-    }
-    cudnn_check(cudnnSetPooling2dDescriptor(
-            desc, mode, CUDNN_NOT_PROPAGATE_NAN, param.window_h, param.window_w,
-            param.pad_h, param.pad_w, param.stride_h, param.stride_w));
-}
-
 LRNDesc::LRNDesc() {
     cudnn_check(cudnnCreateLRNDescriptor(&desc));
 }

dnn/src/cuda/cudnn_wrapper.h

@@ -54,14 +54,6 @@ class ConvDesc {
         cudnnConvolutionDescriptor_t desc;
 };
 
-class PoolingDesc {
-    public:
-        PoolingDesc();
-        void set(const param::Pooling &param);
-        ~PoolingDesc();
-        cudnnPoolingDescriptor_t desc;
-};
-
 class LRNDesc {
     public:
         LRNDesc();

dnn/src/cuda/pooling/algo.cpp

+/**
+ * \file dnn/src/cuda/pooling/algos.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 "./algo.h"
+#include "./pooling2d_qint.cuh"
+#include "src/cuda/utils.h"
+
+using namespace megdnn;
+using namespace cuda;
+
+namespace {
+#define V1(v) #v
+#define V(v) V1(v)
+#define DEF_NAME(NAME) \
+#NAME "v" V(CUDNN_MAJOR) "." V(CUDNN_MINOR) "." V(CUDNN_PATCHLEVEL)
+}  // namespace
+
+PoolingForwardImpl::AlgoPack::AlgoPack() {
+    all_algos.push_back(&algo_chwn4);
+    all_algos.push_back(&algo_nchw4);
+    all_algos.push_back(&algo_nchw32);
+    all_algos.push_back(&algo_nhwc);
+    all_algos.push_back(&algo_nchw64);
+    all_algos.push_back(&algo_cudnn);
+#if CUDNN_VERSION >= 6000
+    all_algos.push_back(&algo_cudnn_max_deterministic);
+#endif
+
+    for (auto&& algo : all_algos) {
+        m_all_algos_map.emplace(algo->info().desc, algo);
+    }
+}
+
+PoolingForwardImpl::AlgoPack PoolingForwardImpl::sm_algo_pack;
+MEGDNN_DEF_GET_ALGO_FROM_DESC(PoolingForwardImpl)
+
+PoolingForwardImpl::AlgoBase::SizeArgs::SizeArgs(PoolingForwardImpl* o,
+                                                 const TensorLayout& src,
+                                                 const TensorLayout& dst)
+        : handle{concrete_handle(o->handle())},
+          opr{o},
+          layout_src{&src},
+          layout_dst{&dst} {}
+
+PoolingForwardImpl::AlgoBase::ExecArgs::ExecArgs(PoolingForwardImpl* opr,
+                                                 _megdnn_tensor_in src,
+                                                 _megdnn_tensor_out dst,
+                                                 _megdnn_workspace workspace)
+        : SizeArgs(opr, src.layout, dst.layout),
+          src_tensor{&src},
+          dst_tensor{&dst},
+          workspace{workspace} {}
+
+std::string PoolingForwardImpl::AlgoBase::SizeArgs::to_string() const {
+    return ssprintf("src=%s, dst=%s", layout_src->to_string().c_str(),
+                    layout_dst->to_string().c_str());
+}
+
+WorkspaceBundle PoolingForwardImpl::AlgoBase::get_workspace_bundle(
+        void* ptr, const SizeArgs& args) const {
+    SmallVector<size_t> sizes;
+    TensorLayout fsrc = *args.layout_src;
+    TensorLayout fdst = *args.layout_dst;
+    auto get_workspace = [&sizes](TensorLayout& layout) {
+        if (layout.dtype == dtype::BFloat16()) {
+            layout.dtype = dtype::Float32();
+            sizes.push_back(layout.span().dist_byte());
+        }
+    };
+    get_workspace(fsrc);
+    get_workspace(fdst);
+    return {ptr, std::move(sizes)};
+}
+
+size_t PoolingForwardImpl::AlgoBase::get_workspace_in_bytes(
+        const SizeArgs& args) const {
+    return get_workspace_bundle(nullptr, args).total_size_in_bytes();
+}
+
+bool PoolingForwardImpl::AlgoCUDNN::is_available(const SizeArgs& args) const {
+    using Format = param::Pooling::Format;
+    return (((args.opr->param().format == Format::NCHW ||
+              args.opr->param().format == Format::NHWC) &&
+             (args.layout_src->dtype.enumv() == DTypeEnum::Float16 ||
+              args.layout_src->dtype.enumv() == DTypeEnum::BFloat16 ||
+              args.layout_src->dtype.enumv() == DTypeEnum::Float32 ||
+              args.layout_src->dtype.enumv() == DTypeEnum::Int8 ||
+              args.layout_src->dtype.enumv() == DTypeEnum::QuantizedS32 ||
+              args.layout_src->dtype.enumv() == DTypeEnum::QuantizedS8 ||
+              args.layout_src->dtype.enumv() == DTypeEnum::Quantized8Asymm)) ||
+            ((args.opr->param().format == Format::NCHW4 ||
+              args.opr->param().format == Format::NCHW32) &&
+             (args.layout_src->dtype.enumv() == DTypeEnum::QuantizedS8 ||
+              args.layout_src->dtype.enumv() == DTypeEnum::Quantized8Asymm)));
+}
+
+void PoolingForwardImpl::AlgoCUDNN::init_mode(const ExecArgs& args,
+                                              cudnnPoolingMode_t& mode) const {
+    switch (args.opr->param().mode) {
+        case param::Pooling::Mode::MAX:
+            mode = CUDNN_POOLING_MAX;
+            break;
+        case param::Pooling::Mode::AVERAGE:
+            mode = CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
+            break;
+        case param::Pooling::Mode::AVERAGE_COUNT_EXCLUDE_PADDING:
+            mode = CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING;
+            break;
+        default:
+            megdnn_throw(ssprintf("Unspport pooling mode : {%d}",
+                                  static_cast<int>(args.opr->param().mode)));
+    }
+}
+
+void PoolingForwardImpl::AlgoCUDNN::exec(const ExecArgs& args) const {
+    TensorND src = *args.src_tensor;
+    TensorND dst = *args.dst_tensor;
+    auto wsb = get_workspace_bundle(args.workspace.raw_ptr, args);
+    auto ctypecvt = CompTypeCvter<dtype::BFloat16, dtype::Float32>(
+            concrete_handle(args.handle), &wsb);
+    if (args.layout_src->dtype.enumv() == DTypeTrait<dtype::BFloat16>::enumv) {
+        ctypecvt.src_to_comp_type(*args.src_tensor, src)
+                .src_to_comp_type(*args.dst_tensor, dst);
+    }
+    {
+        dt_float32 alpha = 1.0f, beta = 0.0f;
+        TensorDesc src_desc, dst_desc;
+        src_desc.set(src.layout, args.opr->param().format);
+        dst_desc.set(dst.layout, args.opr->param().format);
+
+        cudnnPoolingMode_t mode;
+        init_mode(args, mode);
+
+        cudnnPoolingDescriptor_t cudnn_desc;
+        cudnn_check(cudnnCreatePoolingDescriptor(&cudnn_desc));
+        cudnn_check(cudnnSetPooling2dDescriptor(
+                cudnn_desc, mode, CUDNN_NOT_PROPAGATE_NAN,
+                args.opr->param().window_h, args.opr->param().window_w,
+                args.opr->param().pad_h, args.opr->param().pad_w,
+                args.opr->param().stride_h, args.opr->param().stride_w));
+        cudnn_check(cudnnPoolingForward(args.handle->cudnn_handle(), cudnn_desc,
+                                        &alpha, src_desc.desc, src.raw_ptr,
+                                        &beta, dst_desc.desc, dst.raw_ptr));
+        cudnn_check(cudnnDestroyPoolingDescriptor(cudnn_desc));
+    }
+    if (args.layout_src->dtype.enumv() == DTypeTrait<dtype::BFloat16>::enumv) {
+        ctypecvt.comp_to_dst_type(dst, *args.dst_tensor);
+    }
+}
+
+#if CUDNN_VERSION >= 6000
+bool PoolingForwardImpl::AlgoCUDNNMAXDETERMINISTIC::is_available(
+        const SizeArgs& args) const {
+    using Format = param::Pooling::Format;
+    return (args.opr->param().mode == param::Pooling::Mode::MAX &&
+            (((args.opr->param().format == Format::NCHW ||
+               args.opr->param().format == Format::NHWC) &&
+              (args.layout_src->dtype.enumv() == DTypeEnum::Float16 ||
+               args.layout_src->dtype.enumv() == DTypeEnum::BFloat16 ||
+               args.layout_src->dtype.enumv() == DTypeEnum::Float32 ||
+               args.layout_src->dtype.enumv() == DTypeEnum::Int8 ||
+               args.layout_src->dtype.enumv() == DTypeEnum::QuantizedS32 ||
+               args.layout_src->dtype.enumv() == DTypeEnum::QuantizedS8 ||
+               args.layout_src->dtype.enumv() == DTypeEnum::Quantized8Asymm)) ||
+             ((args.opr->param().format == Format::NCHW4 ||
+               args.opr->param().format == Format::NCHW32) &&
+              (args.layout_src->dtype.enumv() == DTypeEnum::QuantizedS8 ||
+               args.layout_src->dtype.enumv() == DTypeEnum::Quantized8Asymm))));
+}
+
+void PoolingForwardImpl::AlgoCUDNNMAXDETERMINISTIC::init_mode(
+        const ExecArgs& args, cudnnPoolingMode_t& mode) const {
+    switch (args.opr->param().mode) {
+        case param::Pooling::Mode::MAX:
+            mode = CUDNN_POOLING_MAX_DETERMINISTIC;
+            break;
+        default:
+            megdnn_throw(ssprintf("Unspport pooling mode : {%d}",
+                                  static_cast<int>(args.opr->param().mode)));
+    }
+}
+
+void PoolingForwardImpl::AlgoCUDNNMAXDETERMINISTIC::exec(
+        const ExecArgs& args) const {
+    TensorND src = *args.src_tensor;
+    TensorND dst = *args.dst_tensor;
+    auto wsb = get_workspace_bundle(args.workspace.raw_ptr, args);
+    auto ctypecvt = CompTypeCvter<dtype::BFloat16, dtype::Float32>(
+            concrete_handle(args.handle), &wsb);
+    if (args.layout_src->dtype.enumv() == DTypeTrait<dtype::BFloat16>::enumv) {
+        ctypecvt.src_to_comp_type(*args.src_tensor, src)
+                .src_to_comp_type(*args.dst_tensor, dst);
+    }
+    {
+        dt_float32 alpha = 1.0f, beta = 0.0f;
+        TensorDesc src_desc, dst_desc;
+        src_desc.set(src.layout, args.opr->param().format);
+        dst_desc.set(dst.layout, args.opr->param().format);
+
+        cudnnPoolingMode_t mode;
+        init_mode(args, mode);
+
+        cudnnPoolingDescriptor_t cudnn_desc;
+        cudnn_check(cudnnCreatePoolingDescriptor(&cudnn_desc));
+        cudnn_check(cudnnSetPooling2dDescriptor(
+                cudnn_desc, mode, CUDNN_NOT_PROPAGATE_NAN,
+                args.opr->param().window_h, args.opr->param().window_w,
+                args.opr->param().pad_h, args.opr->param().pad_w,
+                args.opr->param().stride_h, args.opr->param().stride_w));
+        cudnn_check(cudnnPoolingForward(args.handle->cudnn_handle(), cudnn_desc,
+                                        &alpha, src_desc.desc, src.raw_ptr,
+                                        &beta, dst_desc.desc, dst.raw_ptr));
+        cudnn_check(cudnnDestroyPoolingDescriptor(cudnn_desc));
+    }
+    if (args.layout_src->dtype.enumv() == DTypeTrait<dtype::BFloat16>::enumv) {
+        ctypecvt.comp_to_dst_type(dst, *args.dst_tensor);
+    }
+}
+#endif
+
+bool PoolingForwardImpl::AlgoCHWN4::is_available(const SizeArgs& args) const {
+    using Format = param::Pooling::Format;
+    return (args.opr->param().format == Format::CHWN4 &&
+            (args.layout_src->dtype.enumv() == DTypeEnum::Quantized8Asymm ||
+             args.layout_src->dtype.enumv() == DTypeEnum::QuantizedS8));
+}
+
+void PoolingForwardImpl::AlgoCHWN4::exec(const ExecArgs& args) const {
+    pooling2d::Param kern_param;
+    size_t c = (*args.layout_src)[0], hi = (*args.layout_src)[1],
+           wi = (*args.layout_src)[2], n = (*args.layout_src)[3],
+           ho = (*args.layout_dst)[1], wo = (*args.layout_dst)[2];
+    c = c * 4;
+    size_t ph = args.opr->param().pad_h, pw = args.opr->param().pad_w;
+    size_t window_h = args.opr->param().window_h,
+           window_w = args.opr->param().window_w;
+    size_t sh = args.opr->param().stride_h, sw = args.opr->param().stride_w;
+    kern_param.n = n, kern_param.c = c, kern_param.hi = hi, kern_param.wi = wi,
+    kern_param.ho = ho, kern_param.wo = wo, kern_param.ph = ph,
+    kern_param.pw = pw, kern_param.window_h = window_h,
+    kern_param.window_w = window_w, kern_param.sh = sh, kern_param.sw = sw;
+    auto&& stream = cuda_stream(args.handle);
+    pooling2d::do_pooling2d_int8_cdiv4hwn4(
+            args.src_tensor->compatible_ptr<int8_t>(),
+            args.dst_tensor->compatible_ptr<int8_t>(), kern_param, stream,
+            static_cast<uint32_t>(args.opr->param().mode));
+}
+
+bool PoolingForwardImpl::AlgoNCHW4::is_available(const SizeArgs& args) const {
+    using Format = param::Pooling::Format;
+    return args.opr->param().format == Format::NCHW4 &&
+           (args.layout_src->dtype.enumv() == DTypeEnum::Quantized8Asymm ||
+            args.layout_src->dtype.enumv() == DTypeEnum::QuantizedS8);
+}
+
+void PoolingForwardImpl::AlgoNCHW4::exec(const ExecArgs& args) const {
+    pooling2d::Param kern_param;
+    size_t n = (*args.layout_src)[0], hi = (*args.layout_src)[2],
+           wi = (*args.layout_src)[3], c = (*args.layout_src)[1],
+           ho = (*args.layout_dst)[2], wo = (*args.layout_dst)[3];
+    c = c * 4;
+    size_t ph = args.opr->param().pad_h, pw = args.opr->param().pad_w;
+    size_t window_h = args.opr->param().window_h,
+           window_w = args.opr->param().window_w;
+    size_t sh = args.opr->param().stride_h, sw = args.opr->param().stride_w;
+    kern_param.n = n, kern_param.c = c, kern_param.hi = hi, kern_param.wi = wi,
+    kern_param.ho = ho, kern_param.wo = wo, kern_param.ph = ph,
+    kern_param.pw = pw, kern_param.window_h = window_h,
+    kern_param.window_w = window_w, kern_param.sh = sh, kern_param.sw = sw;
+    auto&& stream = cuda_stream(args.handle);
+    pooling2d::do_pooling2d_int8_ncdiv4hw4(
+            args.src_tensor->compatible_ptr<int8_t>(),
+            args.dst_tensor->compatible_ptr<int8_t>(), kern_param, stream,
+            static_cast<uint32_t>(args.opr->param().mode));
+}
+
+bool PoolingForwardImpl::AlgoNCHW32::is_available(const SizeArgs& args) const {
+    using Format = param::Pooling::Format;
+    return (args.opr->param().format == Format::NCHW32 &&
+            (args.layout_src->dtype.enumv() == DTypeEnum::Quantized8Asymm ||
+             args.layout_src->dtype.enumv() == DTypeEnum::QuantizedS8));
+}
+
+void PoolingForwardImpl::AlgoNCHW32::exec(const ExecArgs& args) const {
+    pooling2d::Param kern_param;
+    size_t n = (*args.layout_src)[0], hi = (*args.layout_src)[2],
+           wi = (*args.layout_src)[3], c = (*args.layout_src)[1],
+           ho = (*args.layout_dst)[2], wo = (*args.layout_dst)[3];
+    c = c * 32;
+    size_t ph = args.opr->param().pad_h, pw = args.opr->param().pad_w;
+    size_t window_h = args.opr->param().window_h,
+           window_w = args.opr->param().window_w;
+    size_t sh = args.opr->param().stride_h, sw = args.opr->param().stride_w;
+    kern_param.n = n, kern_param.c = c, kern_param.hi = hi, kern_param.wi = wi,
+    kern_param.ho = ho, kern_param.wo = wo, kern_param.ph = ph,
+    kern_param.pw = pw, kern_param.window_h = window_h,
+    kern_param.window_w = window_w, kern_param.sh = sh, kern_param.sw = sw;
+    auto&& stream = cuda_stream(args.handle);
+    pooling2d::do_pooling2d_int8_ncdiv32hw32(
+            args.src_tensor->compatible_ptr<int8_t>(),
+            args.dst_tensor->compatible_ptr<int8_t>(), kern_param, stream,
+            static_cast<uint32_t>(args.opr->param().mode));
+}
+
+bool PoolingForwardImpl::AlgoNHWC::is_available(const SizeArgs& args) const {
+    using Format = param::Pooling::Format;
+    return (args.opr->param().format == Format::NHWC &&
+            (args.layout_src->dtype.enumv() == DTypeEnum::Quantized4Asymm ||
+             args.layout_src->dtype.enumv() == DTypeEnum::QuantizedS4));
+}
+
+void PoolingForwardImpl::AlgoNHWC::exec(const ExecArgs& args) const {
+    TensorND src = *args.src_tensor;
+    TensorND dst = *args.dst_tensor;
+    {
+        megdnn_assert(src.layout.dtype.enumv() == dst.layout.dtype.enumv(),
+                      "src and dst dtype must equal");
+        pooling2d::Param kern_param;
+        size_t n = src.layout[0], hi = src.layout[1], wi = src.layout[2],
+               c = src.layout[3], ho = dst.layout[1], wo = dst.layout[2];
+        size_t ph = args.opr->param().pad_h, pw = args.opr->param().pad_w;
+        size_t window_h = args.opr->param().window_h,
+               window_w = args.opr->param().window_w;
+        size_t sh = args.opr->param().stride_h, sw = args.opr->param().stride_w;
+        kern_param.n = n, kern_param.c = c, kern_param.hi = hi,
+        kern_param.wi = wi, kern_param.ho = ho, kern_param.wo = wo,
+        kern_param.ph = ph, kern_param.pw = pw, kern_param.window_h = window_h,
+        kern_param.window_w = window_w, kern_param.sh = sh, kern_param.sw = sw;
+        bool uint_case = false;
+        int zero_point = 0;
+        if (src.layout.dtype.enumv() == DTypeEnum::Quantized4Asymm) {
+            uint_case = true;
+            zero_point =
+                    src.layout.dtype.param<dtype::Quantized4Asymm>().zero_point;
+        }
+        auto&& stream = cuda_stream(args.handle);
+        pooling2d::do_pooling2d_int4_nhwc(
+                (int8_t*)src.raw_ptr, (int8_t*)dst.raw_ptr, kern_param, stream,
+                static_cast<uint32_t>(args.opr->param().mode), uint_case,
+                zero_point);
+    }
+}
+
+inline void PoolingForwardImpl::AlgoNCHW64::deduce_reformat_layout(
+        std::unique_ptr<RelayoutFormat>& relayout,
+        const TensorLayout& src_layout, TensorLayout& dst_layout,
+        RelayoutFormat::Param::Mode mode, const int oc = 0,
+        const int group = 1) const {
+    if (src_layout.ndim > 0) {
+        RelayoutFormat::Param trans_param;
+        trans_param.mode = mode;
+        trans_param.oc = oc;
+        trans_param.group = group;
+        relayout->param() = trans_param;
+        relayout->deduce_layout(src_layout, dst_layout);
+    } else {
+        dst_layout = src_layout;
+    }
+}
+
+void PoolingForwardImpl::AlgoNCHW64::get_inner_layout(
+        const TensorLayout& src, const TensorLayout& dst,
+        TensorLayout& inner_src, TensorLayout& inner_dst, Handle* handle,
+        PoolingForwardImpl::Param::Format format) const {
+    auto relayout_opr = handle->create_operator<RelayoutFormat>();
+    deduce_reformat_layout(relayout_opr, src, inner_src,
+                           RelayoutFormat::Param::Mode::NCHW_NCHW64, 0, 1);
+    deduce_reformat_layout(relayout_opr, dst, inner_dst,
+                           RelayoutFormat::Param::Mode::NCHW_NCHW64, 0, 1);
+}
+
+WorkspaceBundle PoolingForwardImpl::AlgoNCHW64::get_workspace_bundle(
+        void* ptr, const SizeArgs& args) const {
+    using Format = param::Pooling::Format;
+    SmallVector<size_t> sizes;
+    TensorLayout fsrc = *args.layout_src;
+    TensorLayout fdst = *args.layout_dst;
+    if (args.opr->param().format == Format::NCHW) {
+        get_inner_layout(*args.layout_src, *args.layout_dst, fsrc, fdst,
+                         args.handle, args.opr->param().format);
+        sizes.push_back(fsrc.span().dist_byte());
+        sizes.push_back(fdst.span().dist_byte());
+    }
+    return {ptr, std::move(sizes)};
+}
+
+bool PoolingForwardImpl::AlgoNCHW64::is_available(const SizeArgs& args) const {
+    using Format = param::Pooling::Format;
+    return ((args.opr->param().format == Format::NCHW ||
+             args.opr->param().format == Format::NCHW64) &&
+            (args.layout_src->dtype.enumv() == DTypeEnum::QuantizedS4 ||
+             args.layout_src->dtype.enumv() == DTypeEnum::Quantized4Asymm) &&
+            (args.layout_dst->dtype.enumv() == DTypeEnum::QuantizedS4 ||
+             args.layout_dst->dtype.enumv() == DTypeEnum::Quantized4Asymm));
+}
+
+void PoolingForwardImpl::AlgoNCHW64::exec(const ExecArgs& args) const {
+    using Format = param::Pooling::Format;
+    TensorND src = *args.src_tensor;
+    TensorND dst = *args.dst_tensor;
+    if (args.opr->param().format == Format::NCHW) {
+        auto wsb = get_workspace_bundle(args.workspace.raw_ptr, args);
+        auto handle_ptr = args.handle;
+        get_inner_layout(*args.layout_src, *args.layout_dst, src.layout,
+                         dst.layout, handle_ptr, args.opr->param().format);
+        src.raw_ptr = wsb.get(0);
+        dst.raw_ptr = wsb.get(1);
+        auto relayout_opr = handle_ptr->create_operator<RelayoutFormat>();
+        RelayoutFormat::Param trans_param;
+        trans_param.mode = RelayoutFormat::Param::Mode::NCHW_NCHW64;
+        relayout_opr->param() = trans_param;
+        relayout_opr->exec(*args.src_tensor, src, {});
+    }
+
+    {
+        pooling2d::Param kern_param;
+        size_t n = src.layout[0], hi = src.layout[2], wi = src.layout[3],
+               c = src.layout[1], ho = dst.layout[2], wo = dst.layout[3];
+        c = c * 64;
+        size_t ph = args.opr->param().pad_h, pw = args.opr->param().pad_w;
+        size_t window_h = args.opr->param().window_h,
+               window_w = args.opr->param().window_w;
+        size_t sh = args.opr->param().stride_h, sw = args.opr->param().stride_w;
+        kern_param.n = n, kern_param.c = c, kern_param.hi = hi,
+        kern_param.wi = wi, kern_param.ho = ho, kern_param.wo = wo,
+        kern_param.ph = ph, kern_param.pw = pw, kern_param.window_h = window_h,
+        kern_param.window_w = window_w, kern_param.sh = sh, kern_param.sw = sw;
+        bool uint_case = false;
+        int zero_point = 0;
+        if (src.layout.dtype.enumv() == DTypeEnum::Quantized4Asymm) {
+            uint_case = true;
+            zero_point =
+                    src.layout.dtype.param<dtype::Quantized4Asymm>().zero_point;
+        }
+        auto&& stream = cuda_stream(args.handle);
+        pooling2d::do_pooling2d_int4_ncdiv64hw64(
+                (int8_t*)src.raw_ptr, (int8_t*)dst.raw_ptr, kern_param, stream,
+                static_cast<uint32_t>(args.opr->param().mode), uint_case,
+                zero_point);
+    }
+    if (args.layout_dst->ndim == 4) {
+        auto relayout_opr = args.handle->create_operator<RelayoutFormat>();
+        RelayoutFormat::Param trans_param;
+        trans_param.mode = RelayoutFormat::Param::Mode::NCHW64_NCHW;
+        relayout_opr->param() = trans_param;
+        relayout_opr->exec(dst, *args.dst_tensor, {});
+    }
+}
+
+PoolingBackwardImpl::AlgoPack::AlgoPack() {
+    algo_cudnn.push_back({DEF_NAME(cudnnUnreproducible), false});
+    algo_cudnn.push_back({DEF_NAME(cudnnReproducible), true});
+
+    for (auto&& i : algo_cudnn) {
+        all_algos.push_back(&i);
+    }
+
+    for (auto&& algo : all_algos) {
+        m_all_algos_map.emplace(algo->info().desc, algo);
+    }
+}
+
+PoolingBackwardImpl::AlgoPack PoolingBackwardImpl::sm_algo_pack;
+MEGDNN_DEF_GET_ALGO_FROM_DESC(PoolingBackwardImpl)
+
+PoolingBackwardImpl::AlgoBase::SizeArgs::SizeArgs(PoolingBackwardImpl* o,
+                                                  const TensorLayout& src,
+                                                  const TensorLayout& dst,
+                                                  const TensorLayout& diff,
+                                                  const TensorLayout& grad)
+        : handle{concrete_handle(o->handle())},
+          opr{o},
+          layout_src{&src},
+          layout_dst{&dst},
+          layout_diff{&diff},
+          layout_grad{&grad} {}
+
+PoolingBackwardImpl::AlgoBase::ExecArgs::ExecArgs(PoolingBackwardImpl* opr,
+                                                  _megdnn_tensor_in src,
+                                                  _megdnn_tensor_in dst,
+                                                  _megdnn_tensor_in diff,
+                                                  _megdnn_tensor_out grad,
+                                                  _megdnn_workspace workspace)
+        : SizeArgs(opr, src.layout, dst.layout, diff.layout, grad.layout),
+          src_tensor{&src},
+          dst_tensor{&dst},
+          diff_tensor{&diff},
+          grad_tensor{&grad},
+          workspace{workspace} {}
+
+std::string PoolingBackwardImpl::AlgoBase::SizeArgs::to_string() const {
+    return ssprintf(
+            "src=%s, dst=%s, diff=%s, grad=%s", layout_src->to_string().c_str(),
+            layout_dst->to_string().c_str(), layout_diff->to_string().c_str(),
+            layout_grad->to_string().c_str());
+}
+
+bool PoolingBackwardImpl::AlgoCUDNN::is_available(const SizeArgs& args) const {
+    using Format = param::Pooling::Format;
+#if CUDNN_VERSION < 6000
+    return ((args.opr->param().format == Format::NCHW ||
+             args.opr->param().format == Format::NHWC ||
+             args.opr->param().format == Format::NCHW4 ||
+             args.opr->param().format == Format::NCHW32) &&
+            (m_is_reproducible ^
+             (args.opr->param().mode == param::Pooling::Mode::MAX)));
+#else
+    return ((args.opr->param().format == Format::NCHW ||
+             args.opr->param().format == Format::NHWC ||
+             args.opr->param().format == Format::NCHW4 ||
+             args.opr->param().format == Format::NCHW32) &&
+            (m_is_reproducible ||
+             args.opr->param().mode == param::Pooling::Mode::MAX));
+#endif
+}
+
+WorkspaceBundle PoolingBackwardImpl::AlgoBase::get_workspace_bundle(
+        void* ptr, const SizeArgs& args) const {
+    SmallVector<size_t> sizes;
+    TensorLayout fsrc = *args.layout_src;
+    TensorLayout fdst = *args.layout_dst;
+    TensorLayout fdiff = *args.layout_diff;
+    TensorLayout fgrad = *args.layout_grad;
+    auto get_workspace = [&sizes](TensorLayout& layout) {
+        if (layout.dtype == dtype::BFloat16()) {
+            layout.dtype = dtype::Float32();
+            sizes.push_back(layout.span().dist_byte());
+        }
+    };
+    get_workspace(fsrc);
+    get_workspace(fdst);
+    get_workspace(fdiff);
+    get_workspace(fgrad);
+    return {ptr, std::move(sizes)};
+}
+
+size_t PoolingBackwardImpl::AlgoBase::get_workspace_in_bytes(
+        const SizeArgs& args) const {
+    return get_workspace_bundle(nullptr, args).total_size_in_bytes();
+}
+
+void PoolingBackwardImpl::AlgoCUDNN::init_mode(const ExecArgs& args,
+                                               cudnnPoolingMode_t& mode) const {
+    if (m_is_reproducible) {
+        switch (args.opr->param().mode) {
+#if CUDNN_VERSION >= 6000
+            case param::Pooling::Mode::MAX:
+                mode = CUDNN_POOLING_MAX_DETERMINISTIC;
+                break;
+#endif
+            case param::Pooling::Mode::AVERAGE:
+                mode = CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
+                break;
+            case param::Pooling::Mode::AVERAGE_COUNT_EXCLUDE_PADDING:
+                mode = CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING;
+                break;
+            default:
+                megdnn_throw(
+                        ssprintf("Unspport pooling mode : {%d}",
+                                 static_cast<int>(args.opr->param().mode)));
+        }
+    } else if (args.opr->param().mode == param::Pooling::Mode::MAX) {
+        mode = CUDNN_POOLING_MAX;
+    } else {
+        megdnn_throw("init_mode failed\n");
+    }
+}
+
+void PoolingBackwardImpl::AlgoCUDNN::exec(const ExecArgs& args) const {
+    TensorND src = *args.src_tensor;
+    TensorND dst = *args.dst_tensor;
+    TensorND diff = *args.diff_tensor;
+    TensorND grad = *args.grad_tensor;
+    auto wsb = get_workspace_bundle(args.workspace.raw_ptr, args);
+    auto ctypecvt = CompTypeCvter<dtype::BFloat16, dtype::Float32>(
+            concrete_handle(args.handle), &wsb);
+    if (args.layout_src->dtype.enumv() == DTypeTrait<dtype::BFloat16>::enumv) {
+        ctypecvt.src_to_comp_type(*args.src_tensor, src)
+                .src_to_comp_type(*args.dst_tensor, dst)
+                .src_to_comp_type(*args.diff_tensor, diff)
+                .src_to_comp_type(*args.grad_tensor, grad);
+    }
+    {
+        dt_float32 alpha = 1.0f, beta = 0.0f;
+        TensorDesc src_desc, dst_desc, diff_desc, grad_desc;
+        src_desc.set(src.layout, args.opr->param().format);
+        dst_desc.set(dst.layout, args.opr->param().format);
+        diff_desc.set(diff.layout, args.opr->param().format);
+        grad_desc.set(grad.layout, args.opr->param().format);
+
+        cudnnPoolingMode_t mode;
+        init_mode(args, mode);
+
+        cudnnPoolingDescriptor_t cudnn_desc;
+        cudnn_check(cudnnCreatePoolingDescriptor(&cudnn_desc));
+        cudnn_check(cudnnSetPooling2dDescriptor(
+                cudnn_desc, mode, CUDNN_NOT_PROPAGATE_NAN,
+                args.opr->param().window_h, args.opr->param().window_w,
+                args.opr->param().pad_h, args.opr->param().pad_w,
+                args.opr->param().stride_h, args.opr->param().stride_w));
+        cudnn_check(cudnnPoolingBackward(
+                args.handle->cudnn_handle(), cudnn_desc, &alpha, dst_desc.desc,
+                dst.raw_ptr, diff_desc.desc, diff.raw_ptr, src_desc.desc,
+                src.raw_ptr, &beta, grad_desc.desc, grad.raw_ptr));
+        cudnn_check(cudnnDestroyPoolingDescriptor(cudnn_desc));
+    }
+    if (args.layout_src->dtype.enumv() == DTypeTrait<dtype::BFloat16>::enumv) {
+        ctypecvt.comp_to_dst_type(grad, *args.grad_tensor);
+    }
+}
+
+// vim: syntax=cpp.doxygen

dnn/src/cuda/pooling/algo.h

+/**
+ * \file dnn/src/cuda/pooling/algo.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
+
+#include <unordered_map>
+#include "src/common/algo_base.h"
+#include "src/common/metahelper.h"
+#include "src/cuda/cudnn_wrapper.h"
+#include "src/cuda/pooling/opr_impl.h"
+
+namespace megdnn {
+namespace cuda {
+
+namespace {
+#define V1(v) #v
+#define V(v) V1(v)
+#define DEF_NAME(NAME) \
+#NAME "v" V(CUDNN_MAJOR) "." V(CUDNN_MINOR) "." V(CUDNN_PATCHLEVEL)
+}  // namespace
+
+class PoolingForwardImpl::AlgoBase : public Algorithm {
+public:
+    enum class AlgoType : uint32_t {
+        CUDA_CUDNN,
+#if CUDNN_VERSION >= 6000
+        CUDA_CUDNN_MAXDETERMINISTIC,
+#endif
+        CUDA_CHWN4,
+        CUDA_NCHW4,
+        CUDA_NCHW32,
+        CUDA_NHWC,
+        CUDA_NCHW64
+    };
+    using Mapper = std::unordered_map<AlgorithmDesc, AlgoBase*>;
+
+    AlgoBase() : Algorithm() { m_handle_type = Handle::HandleType::CUDA; }
+    struct SizeArgs {
+        HandleImpl* handle;
+        PoolingForwardImpl* opr;
+        const TensorLayout *layout_src, *layout_dst;
+
+        std::string to_string() const;
+        SizeArgs(PoolingForwardImpl* opr, const TensorLayout& src,
+                 const TensorLayout& dst);
+    };
+    struct ExecArgs : public SizeArgs {
+        const TensorND *src_tensor, *dst_tensor;
+        Workspace workspace;
+
+        ExecArgs(PoolingForwardImpl* opr, _megdnn_tensor_in src,
+                 _megdnn_tensor_out dst, _megdnn_workspace workspace);
+    };
+
+    virtual bool is_available(const SizeArgs& args) const = 0;
+    size_t get_workspace_in_bytes(const SizeArgs& args) const;
+    virtual void exec(const ExecArgs& args) const = 0;
+
+    bool is_available_attribute(
+            const SizeArgs& args,
+            const AlgoAttribute& positive_attr = AlgoAttribute::REPRODUCIBLE,
+            const AlgoAttribute& negative_attr = AlgoAttribute::DEFAULT) {
+        return contain_attribute_all(positive_attr) &&
+               !contain_attribute_any(negative_attr) && is_available(args);
+    }
+
+protected:
+    ~AlgoBase() = default;
+    virtual WorkspaceBundle get_workspace_bundle(void* ptr,
+                                                 const SizeArgs& args) const;
+};
+
+class PoolingForwardImpl::AlgoCUDNN final : public AlgoBase {
+    std::string m_algo_name;
+
+public:
+    AlgoCUDNN(std::string name) : m_algo_name(name) {}
+
+    bool is_available(const SizeArgs& args) const override;
+    void init_mode(const ExecArgs& args, cudnnPoolingMode_t& mode) const;
+    void exec(const ExecArgs& args) const override;
+
+    const char* name() const override { return m_algo_name.c_str(); }
+    AlgoAttribute attribute() const override {
+        return AlgoAttribute::REPRODUCIBLE;
+    }
+
+    MEGDNN_DECL_ALGO_TYPE(CUDA_CUDNN)
+
+    std::string param() const override { return m_algo_name; }
+};
+
+#if CUDNN_VERSION >= 6000
+class PoolingForwardImpl::AlgoCUDNNMAXDETERMINISTIC final : public AlgoBase {
+    std::string m_algo_name;
+
+public:
+    AlgoCUDNNMAXDETERMINISTIC(std::string name) : m_algo_name(name) {}
+
+    bool is_available(const SizeArgs& args) const override;
+    void init_mode(const ExecArgs& args, cudnnPoolingMode_t& mode) const;
+    void exec(const ExecArgs& args) const override;
+
+    const char* name() const override { return m_algo_name.c_str(); }
+    AlgoAttribute attribute() const override {
+        return AlgoAttribute::REPRODUCIBLE;
+    }
+
+    MEGDNN_DECL_ALGO_TYPE(CUDA_CUDNN_MAXDETERMINISTIC)
+
+    std::string param() const override { return m_algo_name; }
+};
+#endif
+
+#define ALGO_LAYOUT_POOLING_IMPL(_layout)                                 \
+    class PoolingForwardImpl::Algo##_layout final : public AlgoBase {     \
+        std::string m_algo_name;                                          \
+                                                                          \
+    public:                                                               \
+        Algo##_layout(                                                    \
+                std::string name = std::string("CUDA_").append(#_layout)) \
+                : m_algo_name(name) {}                                    \
+        bool is_available(const SizeArgs& args) const override;           \
+        void exec(const ExecArgs& args) const override;                   \
+        const char* name() const override { return m_algo_name.c_str(); } \
+        AlgoAttribute attribute() const override {                        \
+            return AlgoAttribute::REPRODUCIBLE;                           \
+        }                                                                 \
+        MEGDNN_DECL_ALGO_TYPE(CUDA_##_layout)
+
+ALGO_LAYOUT_POOLING_IMPL(CHWN4)};
+ALGO_LAYOUT_POOLING_IMPL(NCHW4)};
+ALGO_LAYOUT_POOLING_IMPL(NCHW32)};
+ALGO_LAYOUT_POOLING_IMPL(NHWC)};
+ALGO_LAYOUT_POOLING_IMPL(NCHW64) //{
+protected:
+    WorkspaceBundle get_workspace_bundle(void* ptr, const SizeArgs& args)
+            const override;
+
+private:
+    inline void deduce_reformat_layout(
+            std::unique_ptr<RelayoutFormat> & relayout,
+            const TensorLayout& src_layout, TensorLayout& dst_layout,
+            RelayoutFormat::Param::Mode mode, const int oc, const int group)
+            const;
+    void get_inner_layout(const TensorLayout& src, const TensorLayout& dst,
+                          TensorLayout& inner_src, TensorLayout& inner_dst,
+                          Handle* handle,
+                          PoolingForwardImpl::Param::Format format) const;
+};
+
+#undef ALGO_LAYOUT_POOLING_IMPL
+
+class PoolingForwardImpl::AlgoPack : NonCopyableObj {
+private:
+    AlgoBase::Mapper m_all_algos_map;
+
+public:
+    AlgoPack();
+    AlgoCUDNN algo_cudnn{DEF_NAME(cudnnForward)};
+#if CUDNN_VERSION >= 6000
+    AlgoCUDNNMAXDETERMINISTIC algo_cudnn_max_deterministic{
+            DEF_NAME(cudnnForwardMaxDeterministic)};
+#endif
+    AlgoCHWN4 algo_chwn4;
+    AlgoNCHW4 algo_nchw4;
+    AlgoNCHW32 algo_nchw32;
+    AlgoNHWC algo_nhwc;
+    AlgoNCHW64 algo_nchw64;
+
+    std::vector<AlgoBase*> all_algos;
+
+    const AlgoBase::Mapper& all_algos_map() const { return m_all_algos_map; }
+};
+
+class PoolingBackwardImpl::AlgoBase : public Algorithm {
+public:
+    enum class AlgoType : uint32_t { CUDA_CUDNN };
+    using Mapper = std::unordered_map<AlgorithmDesc, AlgoBase*>;
+
+    AlgoBase() : Algorithm() { m_handle_type = Handle::HandleType::CUDA; }
+    struct SizeArgs {
+        HandleImpl* handle;
+        PoolingBackwardImpl* opr;
+        const TensorLayout *layout_src, *layout_dst, *layout_diff, *layout_grad;
+
+        std::string to_string() const;
+        SizeArgs(PoolingBackwardImpl* opr, const TensorLayout& src,
+                 const TensorLayout& dst, const TensorLayout& diff,
+                 const TensorLayout& grad);
+    };
+    struct ExecArgs : public SizeArgs {
+        const TensorND *src_tensor, *dst_tensor, *diff_tensor, *grad_tensor;
+        Workspace workspace;
+
+        ExecArgs(PoolingBackwardImpl* opr, _megdnn_tensor_in src,
+                 _megdnn_tensor_in dst, _megdnn_tensor_in diff,
+                 _megdnn_tensor_out grad, _megdnn_workspace workspace);
+    };
+
+    virtual bool is_available(const SizeArgs& args) const = 0;
+    size_t get_workspace_in_bytes(const SizeArgs& args) const;
+    virtual void exec(const ExecArgs& args) const = 0;
+
+    bool is_available_attribute(
+            const SizeArgs& args,
+            const AlgoAttribute& positive_attr = AlgoAttribute::REPRODUCIBLE,
+            const AlgoAttribute& negative_attr = AlgoAttribute::DEFAULT) {
+        return contain_attribute_all(positive_attr) &&
+               !contain_attribute_any(negative_attr) && is_available(args);
+    }
+
+protected:
+    ~AlgoBase() = default;
+    virtual WorkspaceBundle get_workspace_bundle(void* ptr,
+                                                 const SizeArgs& args) const;
+};
+
+class PoolingBackwardImpl::AlgoCUDNN final : public AlgoBase {
+    std::string m_algo_name;
+    bool m_is_reproducible;
+
+public:
+    AlgoCUDNN(std::string name, bool is_reproducible)
+            : m_algo_name(name), m_is_reproducible(is_reproducible) {}
+
+    bool is_available(const SizeArgs& args) const override;
+    void init_mode(const ExecArgs& args, cudnnPoolingMode_t& mode) const;
+    void exec(const ExecArgs& args) const override;
+
+    const char* name() const override { return m_algo_name.c_str(); }
+    AlgoAttribute attribute() const override {
+        auto ret = AlgoAttribute::DEFAULT;
+        if (m_is_reproducible) {
+            ret |= AlgoAttribute::REPRODUCIBLE;
+        }
+        return ret;
+    }
+
+    MEGDNN_DECL_ALGO_TYPE(CUDA_CUDNN)
+
+    std::string param() const override { return m_algo_name; }
+};
+
+class PoolingBackwardImpl::AlgoPack : NonCopyableObj {
+private:
+    AlgoBase::Mapper m_all_algos_map;
+
+public:
+    AlgoPack();
+    std::vector<AlgoCUDNN> algo_cudnn;
+    std::vector<AlgoBase*> all_algos;
+
+    const AlgoBase::Mapper& all_algos_map() const { return m_all_algos_map; }
+};
+
+}  // namespace cuda
+}  // namespace megdnn
+
+// vim: syntax=cpp.doxygen

dnn/src/cuda/pooling/opr_impl.cpp

@@ -6,275 +6,97 @@
  *
  * 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.
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ * implied.
  */
 #include "src/cuda/pooling/opr_impl.h"
-#include "src/cuda/relayout_format/opr_impl.h"
-
+#include "./algo.h"
 #include "./pooling2d_qint.cuh"
+#include "src/common/algo_chooser.h"
+#include "src/cuda/relayout_format/opr_impl.h"
 #include "src/cuda/utils.h"
 
 namespace megdnn {
 namespace cuda {
 
-namespace {
-inline void deduce_reformat_layout(std::unique_ptr<RelayoutFormat>& relayout,
-                                   const TensorLayout& src_layout,
-                                   TensorLayout& dst_layout,
-                                   RelayoutFormat::Param::Mode mode,
-                                   const int oc = 0, const int group = 1) {
-    if (src_layout.ndim > 0) {
-        RelayoutFormat::Param trans_param;
-        trans_param.mode = mode;
-        trans_param.oc = oc;
-        trans_param.group = group;
-        relayout->param() = trans_param;
-        relayout->deduce_layout(src_layout, dst_layout);
-    } else {
-        dst_layout = src_layout;
-    }
+size_t PoolingForwardImpl::get_workspace_in_bytes(const TensorLayout& src,
+                                                  const TensorLayout& dst) {
+    AlgoBase::SizeArgs args(this, src, dst);
+    return get_algorithm(this, src, dst)->get_workspace_in_bytes(args);
 }
 
-void get_inner_layout(const TensorLayout& src, const TensorLayout& dst,
-                      TensorLayout& inner_src, TensorLayout& inner_dst,
-                      Handle* handle,
-                      PoolingForwardImpl::Param::Format format) {
-    bool is_nchw = format == PoolingForwardImpl::Param::Format::NCHW;
-    if (is_nchw) {
-        auto relayout_opr = handle->create_operator<RelayoutFormat>();
-        deduce_reformat_layout(relayout_opr, src, inner_src,
-                               RelayoutFormat::Param::Mode::NCHW_NCHW64, 0, 1);
-        deduce_reformat_layout(relayout_opr, dst, inner_dst,
-                               RelayoutFormat::Param::Mode::NCHW_NCHW64, 0, 1);
-    } else {
-        megdnn_assert(0, "not support");
-    }
+const char* PoolingForwardImpl::get_algorithm_set_name() const {
+    return "CUDA_POOLING_FORWARD";
 }
 
-}  // namespace
-void PoolingForwardImpl::setup_descs(const TensorLayout& src,
-                                     const TensorLayout& dst) {
-    src_desc.set(src, param().format);
-    dst_desc.set(dst, param().format);
-    pooling_desc.set(this->param());
+std::vector<PoolingForwardImpl::Algorithm*>
+PoolingForwardImpl::get_all_algorithms(const TensorLayout& src,
+                                       const TensorLayout& dst) {
+    return megdnn::get_all_algorithms<PoolingForwardImpl>({this, src, dst});
 }
 
-WorkspaceBundle PoolingForwardImpl::get_workspace_bundle(
-        void* ptr, const TensorLayout& src, const TensorLayout& dst) const {
-    SmallVector<size_t> sizes;
-    TensorLayout fsrc = src;
-    TensorLayout fdst = dst;
-    bool is_nchw = param().format == Param::Format::NCHW;
-    if ((src.dtype.enumv() == DTypeEnum::QuantizedS4 ||
-         src.dtype.enumv() == DTypeEnum::Quantized4Asymm) &&
-        (dst.dtype.enumv() == DTypeEnum::QuantizedS4 ||
-         dst.dtype.enumv() == DTypeEnum::Quantized4Asymm) &&
-        is_nchw) {
-        get_inner_layout(src, dst, fsrc, fdst, handle(), param().format);
-        sizes.push_back(fsrc.span().dist_byte());
-        sizes.push_back(fdst.span().dist_byte());
-    } else {
-        auto get_workspace = [&sizes](TensorLayout& layout) {
-            if (layout.dtype == dtype::BFloat16()) {
-                layout.dtype = dtype::Float32();
-                sizes.push_back(layout.span().dist_byte());
-            }
-        };
-        get_workspace(fsrc);
-        get_workspace(fdst);
+PoolingForwardImpl::Algorithm* PoolingForwardImpl::get_algorithm_heuristic(
+        const TensorLayout& src, const TensorLayout& dst,
+        size_t workspace_limit_in_bytes, const AlgoAttribute& positive_attr,
+        const AlgoAttribute& negative_attr) {
+    MEGDNN_MARK_USED_VAR(workspace_limit_in_bytes);
+
+    AlgoBase::SizeArgs args(this, src, dst);
+    for (auto&& iter : sm_algo_pack.all_algos) {
+        if (iter->is_available_attribute(args, positive_attr, negative_attr)) {
+            return iter;
+        }
     }
-    return {ptr, std::move(sizes)};
+    megdnn_throw(
+            ssprintf("require algorithm with attribute(%s) and without "
+                     "attribute(%s), but can't get suitable algo.\n",
+                     Algorithm::attribute_str(positive_attr).c_str(),
+                     Algorithm::attribute_str(negative_attr).c_str()));
+    return nullptr;
 }
 
 void PoolingForwardImpl::exec(_megdnn_tensor_in ssrc, _megdnn_tensor_out sdst,
                               _megdnn_workspace sworkspace) {
     check_exec(ssrc.layout, sdst.layout, sworkspace.size);
-    TensorND src = ssrc;
-    TensorND dst = sdst;
-    Param::Format inner_format = param().format;
-    auto wsb =
-            get_workspace_bundle(sworkspace.raw_ptr, ssrc.layout, sdst.layout);
-    auto ctypecvt = CompTypeCvter<dtype::BFloat16, dtype::Float32>(
-            concrete_handle(this->handle()), &wsb);
-    bool is_nchw = param().format == Param::Format::NCHW;
-    if (ssrc.layout.dtype.enumv() == DTypeTrait<dtype::BFloat16>::enumv) {
-        ctypecvt.src_to_comp_type(ssrc, src).src_to_comp_type(sdst, dst);
-    } else if ((ssrc.layout.dtype.enumv() == DTypeEnum::QuantizedS4 ||
-                ssrc.layout.dtype.enumv() == DTypeEnum::Quantized4Asymm) &&
-               (sdst.layout.dtype.enumv() == DTypeEnum::QuantizedS4 ||
-                sdst.layout.dtype.enumv() == DTypeEnum::Quantized4Asymm) &&
-               is_nchw) {
-        auto handle_ptr = handle();
-        get_inner_layout(ssrc.layout, sdst.layout, src.layout, dst.layout,
-                         handle_ptr, param().format);
-        src.raw_ptr = wsb.get(0);
-        dst.raw_ptr = wsb.get(1);
-        auto relayout_opr = handle_ptr->create_operator<RelayoutFormat>();
-        RelayoutFormat::Param trans_param;
-        trans_param.mode = RelayoutFormat::Param::Mode::NCHW_NCHW64;
-        relayout_opr->param() = trans_param;
-        relayout_opr->exec(ssrc, src, {});
-        inner_format = Param::Format::NCHW64;
-    }
     {
-        using Format = param::Pooling::Format;
-        if (param().format == Format::CHWN4) {
-            pooling2d::Param kern_param;
-            size_t c = src.layout[0], hi = src.layout[1], wi = src.layout[2],
-                   n = src.layout[3], ho = dst.layout[1], wo = dst.layout[2];
-            c = c * 4;
-            size_t ph = param().pad_h, pw = param().pad_w;
-            size_t window_h = param().window_h, window_w = param().window_w;
-            size_t sh = param().stride_h, sw = param().stride_w;
-            kern_param.n = n, kern_param.c = c, kern_param.hi = hi,
-            kern_param.wi = wi, kern_param.ho = ho, kern_param.wo = wo,
-            kern_param.ph = ph, kern_param.pw = pw,
-            kern_param.window_h = window_h, kern_param.window_w = window_w,
-            kern_param.sh = sh, kern_param.sw = sw;
-            auto&& stream = cuda_stream(handle());
-            return pooling2d::do_pooling2d_int8_cdiv4hwn4(
-                    src.compatible_ptr<int8_t>(), dst.compatible_ptr<int8_t>(),
-                    kern_param, stream, static_cast<uint32_t>(param().mode));
-        } else if (param().format == Format::NCHW4) {
-            pooling2d::Param kern_param;
-            size_t n = src.layout[0], hi = src.layout[2], wi = src.layout[3],
-                   c = src.layout[1], ho = dst.layout[2], wo = dst.layout[3];
-            c = c * 4;
-            size_t ph = param().pad_h, pw = param().pad_w;
-            size_t window_h = param().window_h, window_w = param().window_w;
-            size_t sh = param().stride_h, sw = param().stride_w;
-            kern_param.n = n, kern_param.c = c, kern_param.hi = hi,
-            kern_param.wi = wi, kern_param.ho = ho, kern_param.wo = wo,
-            kern_param.ph = ph, kern_param.pw = pw,
-            kern_param.window_h = window_h, kern_param.window_w = window_w,
-            kern_param.sh = sh, kern_param.sw = sw;
-            auto&& stream = cuda_stream(handle());
-            return pooling2d::do_pooling2d_int8_ncdiv4hw4(
-                    src.compatible_ptr<int8_t>(), dst.compatible_ptr<int8_t>(),
-                    kern_param, stream, static_cast<uint32_t>(param().mode));
-        } else if (param().format == Format::NCHW32) {
-            pooling2d::Param kern_param;
-            size_t n = src.layout[0], hi = src.layout[2], wi = src.layout[3],
-                   c = src.layout[1], ho = dst.layout[2], wo = dst.layout[3];
-            c = c * 32;
-            size_t ph = param().pad_h, pw = param().pad_w;
-            size_t window_h = param().window_h, window_w = param().window_w;
-            size_t sh = param().stride_h, sw = param().stride_w;
-            kern_param.n = n, kern_param.c = c, kern_param.hi = hi,
-            kern_param.wi = wi, kern_param.ho = ho, kern_param.wo = wo,
-            kern_param.ph = ph, kern_param.pw = pw,
-            kern_param.window_h = window_h, kern_param.window_w = window_w,
-            kern_param.sh = sh, kern_param.sw = sw;
-            auto&& stream = cuda_stream(handle());
-            return pooling2d::do_pooling2d_int8_ncdiv32hw32(
-                    src.compatible_ptr<int8_t>(), dst.compatible_ptr<int8_t>(),
-                    kern_param, stream, static_cast<uint32_t>(param().mode));
-        } else if (param().format == Format::NCHW64 ||
-                   inner_format == Format::NCHW64) {
-            pooling2d::Param kern_param;
-            size_t n = src.layout[0], hi = src.layout[2], wi = src.layout[3],
-                   c = src.layout[1], ho = dst.layout[2], wo = dst.layout[3];
-            c = c * 64;
-            size_t ph = param().pad_h, pw = param().pad_w;
-            size_t window_h = param().window_h, window_w = param().window_w;
-            size_t sh = param().stride_h, sw = param().stride_w;
-            kern_param.n = n, kern_param.c = c, kern_param.hi = hi,
-            kern_param.wi = wi, kern_param.ho = ho, kern_param.wo = wo,
-            kern_param.ph = ph, kern_param.pw = pw,
-            kern_param.window_h = window_h, kern_param.window_w = window_w,
-            kern_param.sh = sh, kern_param.sw = sw;
-            bool uint_case = false;
-            int zero_point = 0;
-            if (src.layout.dtype.enumv() == DTypeEnum::Quantized4Asymm) {
-                uint_case = true;
-                zero_point = src.layout.dtype.param<dtype::Quantized4Asymm>()
-                                     .zero_point;
-            }
-            auto&& stream = cuda_stream(handle());
-            pooling2d::do_pooling2d_int4_ncdiv64hw64(
-                    (int8_t*)src.raw_ptr, (int8_t*)dst.raw_ptr, kern_param,
-                    stream, static_cast<uint32_t>(param().mode), uint_case,
-                    zero_point);
-            if (sdst.layout.ndim == 4) {
-                auto relayout_opr = handle()->create_operator<RelayoutFormat>();
-                RelayoutFormat::Param trans_param;
-                trans_param.mode = RelayoutFormat::Param::Mode::NCHW64_NCHW;
-                relayout_opr->param() = trans_param;
-                relayout_opr->exec(dst, sdst, {});
-             }
-             return;
-        } else if (param().format == Format::NHWC &&
-                   (src.layout.dtype.enumv() == DTypeEnum::Quantized4Asymm ||
-                    src.layout.dtype.enumv() == DTypeEnum::QuantizedS4)) {
-            megdnn_assert(src.layout.dtype.enumv() == dst.layout.dtype.enumv(),
-                          "src and dst dtype must equal");
-            pooling2d::Param kern_param;
-            size_t n = src.layout[0], hi = src.layout[1], wi = src.layout[2],
-                   c = src.layout[3], ho = dst.layout[1], wo = dst.layout[2];
-            size_t ph = param().pad_h, pw = param().pad_w;
-            size_t window_h = param().window_h, window_w = param().window_w;
-            size_t sh = param().stride_h, sw = param().stride_w;
-            kern_param.n = n, kern_param.c = c, kern_param.hi = hi,
-            kern_param.wi = wi, kern_param.ho = ho, kern_param.wo = wo,
-            kern_param.ph = ph, kern_param.pw = pw,
-            kern_param.window_h = window_h, kern_param.window_w = window_w,
-            kern_param.sh = sh, kern_param.sw = sw;
-            bool uint_case = false;
-            int zero_point = 0;
-            if (src.layout.dtype.enumv() == DTypeEnum::Quantized4Asymm) {
-                uint_case = true;
-                zero_point = src.layout.dtype.param<dtype::Quantized4Asymm>()
-                                     .zero_point;
-            }
-            auto&& stream = cuda_stream(handle());
-            pooling2d::do_pooling2d_int4_nhwc(
-                    (int8_t*)src.raw_ptr, (int8_t*)dst.raw_ptr, kern_param,
-                    stream, static_cast<uint32_t>(param().mode), uint_case,
-                    zero_point);
-            return;
-        }
-        auto handle = cudnn_handle(this->handle());
-        setup_descs(src.layout, dst.layout);
-        dt_float32 alpha = 1.0f, beta = 0.0f;
-        cudnn_check(cudnnPoolingForward(handle, pooling_desc.desc, &alpha,
-                                        src_desc.desc, src.raw_ptr, &beta,
-                                        dst_desc.desc, dst.raw_ptr));
+        AlgoBase::ExecArgs args(this, ssrc, sdst, sworkspace);
+        auto algo = get_algorithm(this, ssrc.layout, sdst.layout);
+        algo->exec(args);
     }
-    if (ssrc.layout.dtype.enumv() == DTypeTrait<dtype::BFloat16>::enumv) {
-        ctypecvt.comp_to_dst_type(dst, sdst);
-    } 
 }
 
-void PoolingBackwardImpl::setup_descs(const TensorLayout& src,
-                                      const TensorLayout& dst,
-                                      const TensorLayout& diff,
-                                      const TensorLayout& grad) {
-    src_desc.set(src);
-    dst_desc.set(dst);
-    diff_desc.set(diff);
-    grad_desc.set(grad);
-    pooling_desc.set(this->param());
+const char* PoolingBackwardImpl::get_algorithm_set_name() const {
+    return "CUDA_POOLING_BACKWARD";
 }
 
-WorkspaceBundle PoolingBackwardImpl::get_workspace_bundle(
-        void* ptr, const TensorLayout& src, const TensorLayout& dst,
-        const TensorLayout& diff, const TensorLayout& grad) const {
-    SmallVector<size_t> sizes;
-    TensorLayout fsrc = src;
-    TensorLayout fdst = dst;
-    TensorLayout fdiff = diff;
-    TensorLayout fgrad = grad;
-    auto get_workspace = [&sizes](TensorLayout& layout) {
-        if (layout.dtype == dtype::BFloat16()) {
-            layout.dtype = dtype::Float32();
-            sizes.push_back(layout.span().dist_byte());
+std::vector<PoolingBackwardImpl::Algorithm*>
+PoolingBackwardImpl::get_all_algorithms(const TensorLayout& src,
+                                        const TensorLayout& dst,
+                                        const TensorLayout& diff,
+                                        const TensorLayout& grad) {
+    return megdnn::get_all_algorithms<PoolingBackwardImpl>(
+            {this, src, dst, diff, grad});
+}
+
+PoolingBackwardImpl::Algorithm* PoolingBackwardImpl::get_algorithm_heuristic(
+        const TensorLayout& src, const TensorLayout& dst,
+        const TensorLayout& diff, const TensorLayout& grad,
+        size_t workspace_limit_in_bytes, const AlgoAttribute& positive_attr,
+        const AlgoAttribute& negative_attr) {
+    MEGDNN_MARK_USED_VAR(workspace_limit_in_bytes);
+
+    AlgoBase::SizeArgs args(this, src, dst, diff, grad);
+    for (auto iter : sm_algo_pack.all_algos) {
+        if (iter->is_available_attribute(args, positive_attr, negative_attr)) {
+            return iter;
         }
-    };
-    get_workspace(fsrc);
-    get_workspace(fdst);
-    get_workspace(fdiff);
-    get_workspace(fgrad);
-    return {ptr, std::move(sizes)};
+    }
+    megdnn_throw(
+            ssprintf("require algorithm with attribute(%s) and without "
+                     "attribute(%s), but can't get suitable algo.\n",
+                     Algorithm::attribute_str(positive_attr).c_str(),
+                     Algorithm::attribute_str(negative_attr).c_str()));
+    return nullptr;
 }
 
 void PoolingBackwardImpl::exec(_megdnn_tensor_in ssrc, _megdnn_tensor_in sdst,
@@ -283,34 +105,23 @@ void PoolingBackwardImpl::exec(_megdnn_tensor_in ssrc, _megdnn_tensor_in sdst,
                                _megdnn_workspace sworkspace) {
     check_exec(ssrc.layout, sdst.layout, sdiff.layout, sgrad.layout,
                sworkspace.size);
-    auto handle = cudnn_handle(this->handle());
-    TensorND src = ssrc;
-    TensorND dst = sdst;
-    TensorND diff = sdiff;
-    TensorND grad = sgrad;
-    auto wsb = get_workspace_bundle(sworkspace.raw_ptr, ssrc.layout,
-                                    sdst.layout, sdiff.layout, sgrad.layout);
-    auto ctypecvt = CompTypeCvter<dtype::BFloat16, dtype::Float32>(
-            concrete_handle(this->handle()), &wsb);
-    if (ssrc.layout.dtype.enumv() == DTypeTrait<dtype::BFloat16>::enumv) {
-        ctypecvt.src_to_comp_type(ssrc, src)
-                .src_to_comp_type(sdst, dst)
-                .src_to_comp_type(sdiff, diff)
-                .src_to_comp_type(sgrad, grad);
-    }
     {
-        setup_descs(src.layout, dst.layout, diff.layout, grad.layout);
-        float alpha = 1.0f, beta = 0.0f;
-        cudnn_check(cudnnPoolingBackward(
-                handle, pooling_desc.desc, &alpha, dst_desc.desc, dst.raw_ptr,
-                diff_desc.desc, diff.raw_ptr, src_desc.desc, src.raw_ptr, &beta,
-                grad_desc.desc, grad.raw_ptr));
-    }
-    if (ssrc.layout.dtype.enumv() == DTypeTrait<dtype::BFloat16>::enumv) {
-        ctypecvt.comp_to_dst_type(grad, sgrad);
+        AlgoBase::ExecArgs args(this, ssrc, sdst, sdiff, sgrad, sworkspace);
+        auto algo = get_algorithm(this, ssrc.layout, sdst.layout, sdiff.layout,
+                                  sgrad.layout);
+        algo->exec(args);
     }
 }
 
+size_t PoolingBackwardImpl::get_workspace_in_bytes(const TensorLayout& src,
+                                                   const TensorLayout& dst,
+                                                   const TensorLayout& diff,
+                                                   const TensorLayout& grad) {
+    AlgoBase::SizeArgs args(this, src, dst, diff, grad);
+    return get_algorithm(this, src, dst, diff, grad)
+            ->get_workspace_in_bytes(args);
+}
+
 }  // namespace cuda
 }  // namespace megdnn
 

dnn/src/cuda/pooling/opr_impl.h

@@ -23,16 +23,45 @@ public:
     void exec(_megdnn_tensor_in src, _megdnn_tensor_out dst,
               _megdnn_workspace workspace) override;
     size_t get_workspace_in_bytes(const TensorLayout& src,
-                                  const TensorLayout& dst) override {
-        return get_workspace_bundle(nullptr, src, dst).total_size_in_bytes();
+                                  const TensorLayout& dst) override;
+
+    const char* get_algorithm_set_name() const override;
+    Algorithm* get_algorithm_from_desc(const AlgorithmDesc& desc) override;
+
+    AlgorithmInfo get_algorithm_info_heuristic(
+            const TensorLayout& src, const TensorLayout& dst,
+            size_t workspace_limit_in_bytes, const AlgoAttribute& positive_attr,
+            const AlgoAttribute& negative_attr) {
+        return get_algorithm_heuristic(src, dst, workspace_limit_in_bytes,
+                                       positive_attr, negative_attr)
+                ->info();
     }
 
+    class AlgoBase;
+    class AlgoCUDNN;
+#if CUDNN_VERSION >= 6000
+    class AlgoCUDNNMAXDETERMINISTIC;
+#endif
+    class AlgoCHWN4;
+    class AlgoNCHW4;
+    class AlgoNCHW32;
+    class AlgoNHWC;
+    class AlgoNCHW64;
+
+    class AlgoPack;
+
+    static const AlgoPack& algo_pack() { return sm_algo_pack; }
+
+protected:
+    std::vector<Algorithm*> get_all_algorithms(
+            const TensorLayout& src, const TensorLayout& dst) override;
+    Algorithm* get_algorithm_heuristic(
+            const TensorLayout& src, const TensorLayout& dst,
+            size_t workspace_limit_in_bytes, const AlgoAttribute& positive_attr,
+            const AlgoAttribute& negative_attr) override;
+
 private:
-    TensorDesc src_desc, dst_desc;
-    PoolingDesc pooling_desc;
-    void setup_descs(const TensorLayout& src, const TensorLayout& dst);
-    WorkspaceBundle get_workspace_bundle(void* ptr, const TensorLayout& src,
-                                         const TensorLayout& dst) const;
+    static AlgoPack sm_algo_pack;
 };
 
 class PoolingBackwardImpl final : public PoolingBackward {
@@ -44,23 +73,43 @@ public:
     size_t get_workspace_in_bytes(const TensorLayout& src,
                                   const TensorLayout& dst,
                                   const TensorLayout& diff,
-                                  const TensorLayout& grad) override {
-        return get_workspace_bundle(nullptr, src, dst, diff, grad)
-                .total_size_in_bytes();
+                                  const TensorLayout& grad) override;
+
+    const char* get_algorithm_set_name() const override;
+    Algorithm* get_algorithm_from_desc(const AlgorithmDesc& desc) override;
+
+    AlgorithmInfo get_algorithm_info_heuristic(
+            const TensorLayout& src, const TensorLayout& dst,
+            const TensorLayout& diff, const TensorLayout& grad,
+            size_t workspace_limit_in_bytes, const AlgoAttribute& positive_attr,
+            const AlgoAttribute& negative_attr) {
+        return get_algorithm_heuristic(src, dst, diff, grad,
+                                       workspace_limit_in_bytes, positive_attr,
+                                       negative_attr)
+                ->info();
     }
 
+    class AlgoBase;
+    class AlgoCUDNN;
+    class AlgoPack;
+
+    static const AlgoPack& algo_pack() { return sm_algo_pack; }
+
+protected:
+    std::vector<Algorithm*> get_all_algorithms(
+            const TensorLayout& src, const TensorLayout& dst,
+            const TensorLayout& diff, const TensorLayout& grad) override;
+    Algorithm* get_algorithm_heuristic(
+            const TensorLayout& src, const TensorLayout& dst,
+            const TensorLayout& diff, const TensorLayout& grad,
+            size_t workspace_limit_in_bytes, const AlgoAttribute& positive_attr,
+            const AlgoAttribute& negative_attr) override;
+
 private:
-    TensorDesc src_desc, dst_desc, diff_desc, grad_desc;
-    PoolingDesc pooling_desc;
-    void setup_descs(const TensorLayout& src, const TensorLayout& dst,
-                     const TensorLayout& diff, const TensorLayout& grad);
-    WorkspaceBundle get_workspace_bundle(void* ptr, const TensorLayout& src,
-                                         const TensorLayout& dst,
-                                         const TensorLayout& diff,
-                                         const TensorLayout& grad) const;
+    static AlgoPack sm_algo_pack;
 };
 
-} // namespace cuda
-} // namespace megdnn
+}  // namespace cuda
+}  // namespace megdnn
 
 // vim: syntax=cpp.doxygen

dnn/src/naive/convolution/algorithms.h

@@ -57,6 +57,24 @@ class DefaultBatchConvBiasForwardAlgorithm final
     const char* name() const override { return "DEFAULT"; }
 };
 
+class DefaultPoolingForwardAlgorithm final
+        : public megdnn::PoolingForward::Algorithm {
+    AlgoAttribute attribute() const override {
+        return AlgoAttribute::REPRODUCIBLE | AlgoAttribute::NAIVE;
+    }
+    uint32_t type() const override { return 0; }
+    const char* name() const override { return "DEFAULT"; }
+};
+
+class DefaultPoolingBackwardAlgorithm final
+        : public megdnn::PoolingBackward::Algorithm {
+    AlgoAttribute attribute() const override {
+        return AlgoAttribute::REPRODUCIBLE | AlgoAttribute::NAIVE;
+    }
+    uint32_t type() const override { return 0; }
+    const char* name() const override { return "DEFAULT"; }
+};
+
 }  // namespace naive
 }  // namespace megdnn
 

dnn/src/naive/handle.cpp

@@ -110,6 +110,9 @@ DefaultLocalShareBackwardFilterAlgorithm
 DefaultMatrixMulAlgorithm HandleImpl::m_default_matmul_fwd_algo;
 DefaultBatchedMatrixMulAlgorithm HandleImpl::m_default_batched_matmul_fwd_algo;
 
+DefaultPoolingForwardAlgorithm HandleImpl::m_default_pooling_fwd_algo;
+DefaultPoolingBackwardAlgorithm HandleImpl::m_default_pooling_bwd_algo;
+
 HandleImpl::HandleImpl(megcoreComputingHandle_t computing_handle,
                        HandleType type)
         : HandleImplHelper(computing_handle, type),

dnn/src/naive/handle.h

@@ -51,6 +51,9 @@ class HandleImpl : public HandleImplHelper {
     static DefaultMatrixMulAlgorithm m_default_matmul_fwd_algo;
     static DefaultBatchedMatrixMulAlgorithm m_default_batched_matmul_fwd_algo;
 
+    static DefaultPoolingForwardAlgorithm m_default_pooling_fwd_algo;
+    static DefaultPoolingBackwardAlgorithm m_default_pooling_bwd_algo;
+
     //! move KernFunc to alloc_kern()->func, destruct func, and call dispatch
     template <typename T>
     void move_kern_func_to_new_kern_and_dispatch(T& func) {
@@ -122,6 +125,14 @@ public:
         return &m_default_batched_matmul_fwd_algo;
     }
 
+    PoolingForward::Algorithm* default_pooling_fwd_algo() {
+        return &m_default_pooling_fwd_algo;
+    }
+
+    PoolingBackward::Algorithm* default_pooling_bwd_algo() {
+        return &m_default_pooling_bwd_algo;
+    }
+
     Relayout* relayout_opr() override {
         return get_helper_opr<Relayout, 2>(this);
     }

dnn/src/naive/pooling/opr_impl.cpp

@@ -582,6 +582,52 @@ void PoolingForwardImpl::exec(_megdnn_tensor_in src, _megdnn_tensor_out dst,
     megdnn_assert_internal(0);
 }
 
+PoolingForward::Algorithm* PoolingForwardImpl::get_algorithm_from_desc(
+        const AlgorithmDesc& desc) {
+    Algorithm* ret =
+            static_cast<HandleImpl*>(handle())->default_pooling_fwd_algo();
+    megdnn_assert(desc == ret->info().desc);
+    return ret;
+}
+
+std::vector<Algorithm*> PoolingForwardImpl::get_all_algorithms(
+        const TensorLayout&, const TensorLayout&) {
+    return {static_cast<HandleImpl*>(handle())->default_pooling_fwd_algo()};
+}
+
+Algorithm* PoolingForwardImpl::get_algorithm_heuristic(
+        const TensorLayout& /*src*/, const TensorLayout& /*dst*/,
+        size_t /*workspace_limit_in_bytes*/, const AlgoAttribute& positive_attr,
+        const AlgoAttribute& negative_attr) {
+    auto algo = static_cast<HandleImpl*>(handle())->default_pooling_fwd_algo();
+    algo->check_attribute(positive_attr, negative_attr);
+    return algo;
+}
+
+Algorithm* PoolingBackwardImpl::get_algorithm_from_desc(
+        const AlgorithmDesc& desc) {
+    Algorithm* ret =
+            static_cast<HandleImpl*>(handle())->default_pooling_bwd_algo();
+    megdnn_assert(desc == ret->info().desc);
+    return ret;
+}
+
+std::vector<Algorithm*> PoolingBackwardImpl::get_all_algorithms(
+        const TensorLayout& /*src*/, const TensorLayout& /*dst*/,
+        const TensorLayout& /*diff*/, const TensorLayout& /*grad*/) {
+    return {static_cast<HandleImpl*>(handle())->default_pooling_bwd_algo()};
+}
+
+Algorithm* PoolingBackwardImpl::get_algorithm_heuristic(
+        const TensorLayout& /*src*/, const TensorLayout& /*dst*/,
+        const TensorLayout& /*diff*/, const TensorLayout& /*grad*/,
+        size_t /*workspace_limit_in_bytes*/, const AlgoAttribute& positive_attr,
+        const AlgoAttribute& negative_attr) {
+    auto algo = static_cast<HandleImpl*>(handle())->default_pooling_bwd_algo();
+    algo->check_attribute(positive_attr, negative_attr);
+    return algo;
+}
+
 WorkspaceBundle PoolingBackwardImpl::get_workspace_bundle(
         void* ptr, const TensorLayout& src, const TensorLayout& dst,
         const TensorLayout& diff, const TensorLayout& grad) const {

dnn/src/naive/pooling/opr_impl.h

@@ -26,6 +26,21 @@ class PoolingForwardImpl: public PoolingForward {
     private:
         WorkspaceBundle get_workspace_bundle(void* ptr, const TensorLayout&,
                                              const TensorLayout&) const;
+
+        const char* get_algorithm_set_name() const override {
+            return "DEFALUT";
+        }
+
+        Algorithm* get_algorithm_from_desc(const AlgorithmDesc&) override;
+
+        std::vector<Algorithm*> get_all_algorithms(
+                const TensorLayout& src, const TensorLayout& dst) override;
+
+        Algorithm* get_algorithm_heuristic(
+                const TensorLayout& src, const TensorLayout& dst,
+                size_t workspace_limit_in_bytes,
+                const AlgoAttribute& positive_attr,
+                const AlgoAttribute& negative_attr) override;
 };
 
 class PoolingBackwardImpl : public PoolingBackward {
@@ -38,6 +53,20 @@ public:
                                   const TensorLayout&,
                                   const TensorLayout&) override;
 
+    const char* get_algorithm_set_name() const override { return "DEFALUT"; }
+
+    Algorithm* get_algorithm_from_desc(const AlgorithmDesc&) override;
+
+    std::vector<Algorithm*> get_all_algorithms(
+            const TensorLayout& src, const TensorLayout& dst,
+            const TensorLayout& diff, const TensorLayout& grad) override;
+
+    Algorithm* get_algorithm_heuristic(
+            const TensorLayout& src, const TensorLayout& dst,
+            const TensorLayout& diff, const TensorLayout& grad,
+            size_t workspace_limit_in_bytes, const AlgoAttribute& positive_attr,
+            const AlgoAttribute& negative_attr) override;
+
 private:
     WorkspaceBundle get_workspace_bundle(void* ptr, const TensorLayout&,
                                          const TensorLayout&,

dnn/test/common/opr_algo_proxy.h

@@ -46,6 +46,12 @@ struct AlgoProxy;
         }                                                                     \
     }
 
+#define LAYOUTS layouts[0], layouts[1]
+#define TENSORS tensors[0], tensors[1]
+DEF_ALGO_PROXY(2);
+#undef LAYOUTS
+#undef TENSORS
+
 #define LAYOUTS layouts[0], layouts[1], layouts[2]
 #define TENSORS tensors[0], tensors[1], tensors[2]
 DEF_ALGO_PROXY(3);

dnn/test/cuda/pooling.cpp

@@ -21,6 +21,13 @@
 #include <cudnn.h>
 #include "test/cuda/benchmark.h"
 
+namespace {
+#define V1(v) #v
+#define V(v) V1(v)
+#define DEF_NAME(NAME) \
+#NAME "v" V(CUDNN_MAJOR) "." V(CUDNN_MINOR) "." V(CUDNN_PATCHLEVEL)
+}  // namespace
+
 namespace megdnn {
 namespace test {
 
@@ -263,19 +270,24 @@ TEST_F(CUDA, POOLING_FORWARD_NCHW_Q4) {
     checker.set_param(param).exec({{20, 24, 22, 33}, {}});
 }
 
-TEST_F(CUDA, POOLING_FORWARD_NCHW4) {
+TEST_F(CUDA, POOLING_FORWARD_NCHW4_NCHW32) {
     require_compute_capability(7, 5);
     using Param = param::Pooling;
     Checker<Pooling> checker(handle_cuda());
     Param param;
     checker.set_dtype(0, dtype::QuantizedS8(0.1f));
-    param.format = Param::Format::NCHW4;
     checker.set_epsilon(1 + 1e-3);
-    checker.set_param(param).exec({{20, 3, 50, 50, 4}, {}});
-    param.mode = Param::Mode::AVERAGE;
-    checker.set_param(param).exec({{20, 3, 50, 50, 4}, {}});
-    param.mode = Param::Mode::AVERAGE_COUNT_EXCLUDE_PADDING;
-    checker.set_param(param).exec({{20, 3, 50, 50, 4}, {}});
+    checker.set_before_exec_callback(
+            AlgoChecker<PoolingForward>(DEF_NAME(cudnnForward)));
+    for (auto format : {Param::Format::NCHW4, Param::Format::NCHW32}) {
+        param.format = format;
+        param.mode = Param::Mode::MAX;
+        checker.set_param(param).exec({{4, 3, 28, 28, 32}, {}});
+        param.mode = Param::Mode::AVERAGE;
+        checker.set_param(param).exec({{4, 3, 28, 28, 64}, {}});
+        param.mode = Param::Mode::AVERAGE_COUNT_EXCLUDE_PADDING;
+        checker.set_param(param).exec({{4, 3, 28, 28, 32}, {}});
+    }
 }
 
 #if CUDNN_VERSION >= 7500
@@ -288,6 +300,8 @@ TEST_F(CUDA, POOLING_FORWARD_NCHW32) {
     auto i8_max = std::numeric_limits<int8_t>().max();
     UniformIntRNG int_rng{i8_min, i8_max};
     checker.set_dtype(0, dtype::QuantizedS8(0.1f));
+    checker.set_before_exec_callback(
+            AlgoChecker<PoolingForward>("CUDA_NCHW32"));
     param.format = Param::Format::NCHW32;
     checker.set_epsilon(1e-3).set_rng(0, &int_rng);
     checker.set_param(param).exec({{64, 8, 28, 28, 32}, {}});
@@ -394,6 +408,7 @@ TEST_F(CUDA, POOLING_FORWARD_INT8_NCHW4) {
     UniformIntRNG int_rng{i8_min, i8_max};
     checker.set_dtype(0, dtype::QuantizedS8(0.1f));
     param.format = Param::Format::NCHW4;
+    checker.set_before_exec_callback(AlgoChecker<PoolingForward>("CUDA_NCHW4"));
     for (auto mode : {Param::Mode::MAX, Param::Mode::AVERAGE,
                       Param::Mode::AVERAGE_COUNT_EXCLUDE_PADDING}) {
         param.mode = mode;
@@ -413,6 +428,8 @@ TEST_F(CUDA, POOLING_FORWARD_INT8_NCHW32) {
     auto i8_max = std::numeric_limits<int8_t>().max();
     UniformIntRNG int_rng{i8_min, i8_max};
     checker.set_dtype(0, dtype::QuantizedS8(0.1f));
+    checker.set_before_exec_callback(
+            AlgoChecker<PoolingForward>("CUDA_NCHW32"));
     param.format = Param::Format::NCHW32;
     for (auto mode : {Param::Mode::MAX, Param::Mode::AVERAGE,
                       Param::Mode::AVERAGE_COUNT_EXCLUDE_PADDING}) {

src/opr/impl/dnn/pooling.cpp

@@ -10,14 +10,52 @@
  */
 #include "megbrain/opr/dnn/pooling.h"
 #include "megbrain/graph/grad_impl.h"
-
+#include "megbrain/opr/search_policy/algo_chooser.h"
 #include "../internal/megdnn_opr_wrapper.inl"
 
+#include "../search_policy/workspace_need_limit_getter.inl"
+
 using namespace mgb;
 using namespace opr;
 
 MGB_DYN_TYPE_OBJ_FINAL_IMPL(PoolingForward);
-MEGDNN_OPR_INIT1(PoolingForward, "pooling")
+
+PoolingForward::PoolingForward(VarNode* i0, const Param& param,
+                               const ExecutionPolicy& policy,
+                               const OperatorNodeConfig& config)
+        : Super(OperatorNodeBaseCtorParam{
+                  i0->owner_graph(), config, "pooling", {i0}}) {
+    init_megdnn_opr(*this, param);
+    add_input({i0});
+    m_policy = policy;
+    
+    intl::MegDNNOprInitPostCtor<PoolingForward>::apply(*this);
+}
+
+SymbolVar PoolingForward::make(SymbolVar i0, const Param& param,
+                               const OperatorNodeConfig& config,
+                               const ExecutionPolicy& policy) {
+    intl::MegDNNOprInitInputsModifier<PoolingForward>::apply(param, {&i0});
+    return i0.insert_single_output_opr<PoolingForward>(i0.node(), param, policy,
+                                                       config);
+}
+
+void PoolingForward::init_output_static_infer_desc() {
+    Super::set_nr_managed_outputs(this->output().size() - 1);
+    Super::Super::init_output_static_infer_desc();
+    init_output_static_infer_desc_workspace(
+            intl::AutoAddWorkspaceNeedLimitGetter<megdnn::PoolingForward>::val);
+}
+
+size_t PoolingForward::get_workspace_size_bytes(
+        const TensorShapeArray& input_shapes,
+        const TensorShapeArray& output_shapes) const {
+    return AlgoChooser<megdnn::PoolingForward>::setup_algo(
+            {TensorLayout{input_shapes[0], input(0)->dtype(),
+                          input(0)->format()},
+             {output_shapes[0], output(0)->dtype(), output(0)->format()}},
+            megdnn_opr(), this, false);
+}
 
 #if MGB_ENABLE_GRAD
 MGB_IMPL_OPR_GRAD(PoolingForward) {
@@ -29,7 +67,41 @@ MGB_IMPL_OPR_GRAD(PoolingForward) {
 #endif
 
 MGB_DYN_TYPE_OBJ_FINAL_IMPL(PoolingBackward);
-MEGDNN_OPR_INIT3(PoolingBackward, "pooling_bwd", 0, true);
+
+PoolingBackward::PoolingBackward(VarNode* i0, VarNode* i1, VarNode* i2,
+                                 const Param& param,
+                                 const ExecutionPolicy& policy,
+                                 const OperatorNodeConfig& config)
+        : Super(
+                  OperatorNodeBaseCtorParam{
+                          i0->owner_graph(), config, "pooling_bwd", {i0}},
+                  0, true) {
+    init_megdnn_opr(*this, param);
+    add_input({i0, i1, i2});
+    intl::MegDNNOprInitPostCtor<PoolingBackward>::apply(*this);
+}
+
+SymbolVar PoolingBackward::make(SymbolVar i0, SymbolVar i1, SymbolVar i2,
+                                const Param& param,
+                                const OperatorNodeConfig& config,
+                                const ExecutionPolicy& policy) {
+    intl::MegDNNOprInitInputsModifier<PoolingBackward>::apply(param,
+                                                              {&i0, &i1, &i2});
+    return i0.insert_single_output_opr<PoolingBackward>(
+            i0.node(), i1.node(), i2.node(), param, policy, config);
+}
+
+size_t PoolingBackward::get_workspace_size_bytes(
+        const TensorShapeArray& input_shapes,
+        const TensorShapeArray& output_shapes) const {
+    return AlgoChooser<megdnn::PoolingBackward>::setup_algo(
+            {TensorLayout{input_shapes[0], input(0)->dtype(),
+                          input(0)->format()},
+             {input_shapes[1], input(1)->dtype(), input(1)->format()},
+             {input_shapes[2], input(2)->dtype(), input(2)->format()},
+             {output_shapes[0], output(0)->dtype(), output(0)->format()}},
+            megdnn_opr(), this, false);
+}
 
 // vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}}
 

src/opr/impl/search_policy/algo_chooser.cpp

@@ -175,6 +175,8 @@ cb(DEFORMABLE_CONV_BACKWARD_DATA, DeformableConvBackwardData);
 cb(DEFORMABLE_CONV_BACKWARD_FILTER, DeformableConvBackwardFilter);
 cb(BATCH_CONV_FORWARD, BatchConvBiasForward);
 cb(CONVBIAS_FORWARD, ConvBiasForward);
+cb(POOLING_FORWARD, PoolingForward);
+cb(POOLING_BACKWARD, PoolingBackward);
 
 #undef cb
 
@@ -195,7 +197,9 @@ cb(CONVBIAS_FORWARD, ConvBiasForward);
     cb(DEFORMABLE_CONV_BACKWARD_DATA, stmt)   \
     cb(DEFORMABLE_CONV_BACKWARD_FILTER, stmt) \
     cb(BATCH_CONV_FORWARD, stmt)              \
-    cb(CONVBIAS_FORWARD, stmt)
+    cb(CONVBIAS_FORWARD, stmt)                \
+    cb(POOLING_FORWARD, stmt)                 \
+    cb(POOLING_BACKWARD, stmt)
 // clang-format on
 
 #define _OPR_TYPE_CASE(_opr_type, _stmt)             \
@@ -521,11 +525,14 @@ AlgoChooser<Opr>::AlgoChooserHelper::AlgoChooserHelper(
 
     mgb_assert(m_fastrun_layouts.size() == layouts.size());
 
-    static_assert(std::tuple_size<FixedTensorLayouts>::value == 3 ||
-                          std::tuple_size<FixedTensorLayouts>::value == 5 ||
-                          std::tuple_size<FixedTensorLayouts>::value == 8,
-                  "Convolution AlgoChooser assumes arity = 3 , 5 or 8 (for "
-                  "deformable conv)");
+    static_assert(
+            std::tuple_size<FixedTensorLayouts>::value == 2 ||
+                    std::tuple_size<FixedTensorLayouts>::value == 3 ||
+                    std::tuple_size<FixedTensorLayouts>::value == 4 ||
+                    std::tuple_size<FixedTensorLayouts>::value == 5 ||
+                    std::tuple_size<FixedTensorLayouts>::value == 8,
+            "Pooling assumes arity = 2 or 4,Convolution AlgoChooser assumes "
+            "arity = 3 , 5 or 8 (for deformable conv)");
 }
 
 template <typename Opr>

src/opr/impl/search_policy/profiler.cpp

@@ -284,7 +284,7 @@ typename TimedProfiler<Opr>::TResult TimedProfiler<Opr>::prof_impl(
                                                         mdn_workspace),
                               std::forward_as_tuple(layouts[0],
                                                     inp_val[1].as_megdnn()),
-                              array_skip<2>(layouts));
+                              array_skip<arity_in>(layouts));
                     });
         }
     });

src/opr/include/megbrain/opr/dnn/pooling.h

@@ -12,34 +12,50 @@
 #pragma once
 
 #include "megbrain/opr/internal/megdnn_opr_wrapper.h"
+#include "megbrain/opr/search_policy/algo_chooser_helper.h"
 #include "megdnn/oprs.h"
 
 namespace mgb {
 namespace opr {
 
 MGB_DEFINE_OPR_CLASS(PoolingForward,
-        intl::MegDNNOprWrapperFwd<megdnn::PoolingForward>) // {
-
-    public:
-        PoolingForward(VarNode *src, const Param &param,
-                const OperatorNodeConfig &config);
-        static SymbolVar make(SymbolVar src, const Param &param,
-                const OperatorNodeConfig &config = {});
+                     intl::MegDNNOprWrapperFwd<megdnn::PoolingForward>,
+                     public mixin::AlgoChooserHelper) //{
+public:
+    PoolingForward(VarNode * src, const Param& param,
+                   const ExecutionPolicy& policy,
+                   const OperatorNodeConfig& config);
+    static SymbolVar make(SymbolVar src, const Param& param,
+                          const OperatorNodeConfig& config = {},
+                          const ExecutionPolicy& policy = {});
+
+    void init_output_static_infer_desc() override;
+
+    size_t get_workspace_size_bytes(const TensorShapeArray& input_shapes,
+                                    const TensorShapeArray& output_shapes)
+            const override;
 };
 using Pooling = PoolingForward;
 
 MGB_DEFINE_OPR_CLASS(PoolingBackward,
-        intl::MegDNNOprWrapperBwd<megdnn::PoolingBackward>) // {
-
-    public:
-        PoolingBackward(VarNode *src, VarNode *dst, VarNode *diff,
-                const Param &param, const OperatorNodeConfig &config);
-        static SymbolVar make(SymbolVar src, SymbolVar dst, SymbolVar diff,
-                const Param &param,
-                const OperatorNodeConfig &config = {});
+                     intl::MegDNNOprWrapperBwd<megdnn::PoolingBackward>,
+                     public mixin::AlgoChooserHelper) //{
+public:
+    PoolingBackward(VarNode * src, VarNode * dst, VarNode * diff,
+                    const Param& param, const ExecutionPolicy& policy,
+                    const OperatorNodeConfig& config);
+    
+    static SymbolVar make(SymbolVar src, SymbolVar dst, SymbolVar diff,
+                          const Param& param,
+                          const OperatorNodeConfig& config = {},
+                          const ExecutionPolicy& policy = {});
+
+    size_t get_workspace_size_bytes(const TensorShapeArray& input_shapes,
+                                    const TensorShapeArray& output_shapes)
+            const override final;
 };
 
-} // namespace opr
-} // namespace mgb
+}  // namespace opr
+}  // namespace mgb
 
 // vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}}

src/opr/include/megbrain/opr/search_policy/algo_chooser.h

@@ -18,6 +18,7 @@
 #include "megbrain/opr/search_policy/algo_chooser_helper.h"
 #include "megbrain/opr/search_policy/profiler.h"
 #include "megbrain/opr/dnn/convolution.h"
+#include "megbrain/opr/dnn/pooling.h"
 #include "megbrain/opr/blas.h"
 #include "megdnn/oprs/base.h"
 

src/opr/include/megbrain/opr/search_policy/profiler.h

@@ -40,7 +40,9 @@ namespace opr {
     cb(DeformableConvBackwardData)   \
     cb(BatchConvBiasForward)         \
     cb(MatrixMul)                    \
-    cb(BatchedMatrixMul)
+    cb(BatchedMatrixMul)             \
+    cb(PoolingForward)               \
+    cb(PoolingBackward)
 // clang-format on
 
 template <typename Opr>

src/opr/test/dnn/pooling.cpp

@@ -11,6 +11,9 @@
 
 #include "./legacy_checker.h"
 #include "megbrain/opr/dnn/pooling.h"
+#include "megbrain/utils/persistent_cache.h"
+#include "megbrain/opr/basic_arith.h"
+#include "megbrain/opr/basic_arith_wrapper.h"
 
 using namespace std;
 using namespace mgb;
@@ -104,6 +107,58 @@ TEST(TestOprDNN, PoolingBackward)
     }
 }
 
+TEST(TestOprDNN, PoolingExePolicy) {
+    using Param = opr::Pooling::Param;
+    Param param;
+    using Policy = opr::Pooling::ExecutionPolicy;
+    using S = Policy::Strategy;
+
+    REQUIRE_GPU(1);
+    auto cn = CompNode::load("gpu0");
+    cn.activate();
+
+    auto orig_impl = PersistentCache::set_impl(
+            std::make_shared<InMemoryPersistentCache>());
+
+    HostTensorND host_y, host_y_copy;
+    S strategy = S::HEURISTIC | S::REPRODUCIBLE;
+
+    auto graph = ComputingGraph::make();
+
+    HostTensorGenerator<> gen;
+    TensorShape shape = {1, 20, 24, 24};
+    auto input = opr::Host2DeviceCopy::make(*graph, gen(shape, cn));
+
+    param.mode = Param::Mode::MAX;
+    param.window_h = param.window_w = 2;
+    param.stride_h = param.stride_w = 2;
+    param.pad_h = param.pad_w = 0;
+    param.format = Param::Format::NCHW;
+
+    Policy policy;
+    policy.strategy = strategy;
+
+    auto pooling = opr::PoolingForward::make(input, param, {}, policy);
+
+    auto loss0 = opr::reduce_sum_sqr(pooling, pooling.make_scalar(1));
+    auto grad = cg::grad(loss0, input, true, false);
+
+    opr::PoolingBackward* found = nullptr;
+    auto cb = [&found](cg::OperatorNodeBase* opr) {
+        if (opr->same_type<opr::PoolingBackward>()) {
+            found = &opr->cast_final_safe<opr::PoolingBackward>();
+        }
+    };
+    cg::DepOprIter{cb}.add(grad.node()->owner_opr());
+    found->set_execution_policy(strategy);
+
+    auto func = graph->compile({make_callback_copy(grad, host_y)});
+    func->execute().wait();
+
+    mgb_assert(found->megdnn_opr()->execution_policy().algo.name.find(
+                       "cudnnReproducible") != std::string::npos);
+}
+
 } // anonymous namespace
 
 // vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}}