refactor(dnn): refactor algo interface, use algoinfo instead of global algorithm

GitOrigin-RevId: 479718ac75

dnn/include/megdnn/oprs/base.h

@@ -6,7 +6,8 @@
  *
  * 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.
  */
 #pragma once
 
@@ -92,24 +93,72 @@ enum class AlgoDataType : uint32_t {
 /*!
  * \brief Abstract representation of an algorithm for implementing
  *      the operator
- *
- * All pointers to Algorithm should be allocated globally and usable
- * across multiple megdnn handles, and they should not be freed by
- * the caller.
  */
 class Algorithm {
 public:
+    static constexpr uint32_t INVALID_ALGO_TYPE = static_cast<uint32_t>(-1);
+    /**
+     * \brief Algorithm information, we can get real algo from
+     * AlgorithmInfo::Info::Desc
+     */
+    struct Info {
+        struct Desc {
+            //! backend of the algo belonging to
+            Handle::HandleType handle_type;
+            //! indicate the real algo implementation
+            uint32_t type = INVALID_ALGO_TYPE;
+            //! serialized param of the algo type
+            std::string param;
+            bool valid() const { return type != INVALID_ALGO_TYPE; }
+            void reset() { type = INVALID_ALGO_TYPE; }
+
+            bool operator==(const Desc& rhs) const {
+                return handle_type == rhs.handle_type && type == rhs.type &&
+                       param == rhs.param;
+            }
+        } desc;
+        //! algorithm name
+        std::string name;
+        bool is_reproducible;
+        bool valid() const { return desc.valid(); }
+        void reset() { desc.reset(); }
+        //! desc donate the algo
+        bool operator==(const Info& rhs) const { return desc == rhs.desc; }
+    };
+
+    virtual ~Algorithm() = default;
+
     /**
      * \brief whether the execution result is
      *      reproducible across multiple runs.
      */
     virtual bool is_reproducible() const = 0;
     virtual const char* name() const = 0;
+    //! serialized param
+    virtual std::string param() const { return {}; }
+    virtual uint32_t type() const = 0;
 
     Handle::HandleType handle_type() const { return m_handle_type; }
+    Info info() const {
+        return {{handle_type(), type(), param()}, name(), is_reproducible()};
+    }
+
+    template <typename T>
+    static void serialize_write_pod(const T& val, std::string& result) {
+        result.append(reinterpret_cast<const char*>(&val), sizeof(T));
+    }
+
+    static void serialize_write_pod(const char* val, std::string& result) {
+        result.append(val, strlen(val));
+    }
+
+    template <typename T>
+    static T deserialize_read_pod(const std::string& data, size_t offset = 0) {
+        T ret = *reinterpret_cast<const T*>(&data[offset]);
+        return ret;
+    }
 
 protected:
-    ~Algorithm() = default;
     Handle::HandleType m_handle_type = Handle::HandleType::NAIVE;
 };
 
@@ -127,6 +176,8 @@ class MultiAlgoOpr;
 template <class Opr>
 class MultiAlgoOpr<Opr, -1> {
 public:
+    using AlgorithmInfo = detail::Algorithm::Info;
+    using AlgorithmDesc = detail::Algorithm::Info::Desc;
     using Algorithm = detail::Algorithm;
     /*!
      * \brief get a string representation for current algorithm set;
@@ -139,8 +190,8 @@ public:
 
     //! policy for executing the operator
     struct ExecutionPolicy {
-        //! nullptr means using heuristic
-        Algorithm* algorithm = nullptr;
+        //! INVALID_ALGO_TYPE algo_type means using heuristic
+        AlgorithmInfo algo;
     };
 
     ExecutionPolicy& execution_policy() { return m_execution_policy; }
@@ -161,6 +212,39 @@ template <class Opr>
 class MultiAlgoOpr<Opr, 3> : public MultiAlgoOpr<Opr, -1> {
 public:
     using Algorithm = detail::Algorithm;
+    using AlgorithmInfo = detail::Algorithm::Info;
+
+    //! get all possible algorithm decriptions for the specified layouts
+    std::vector<AlgorithmInfo> get_all_algorithms_info(const TensorLayout& p0,
+                                                       const TensorLayout& p1,
+                                                       const TensorLayout& p2) {
+        std::vector<AlgorithmInfo> ret;
+        for (auto&& algo : get_all_algorithms(p0, p1, p2)) {
+            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,
+            const TensorLayout& p2,
+            size_t workspace_limit_in_bytes =
+                    std::numeric_limits<size_t>::max(),
+            bool reproducible = false) {
+        return get_algorithm_heuristic(p0, p1, p2, workspace_limit_in_bytes,
+                                       reproducible)
+                ->info();
+    }
+
+protected:
+    ~MultiAlgoOpr() = default;
 
     //! get all possible algorithms for the specified layouts
     virtual std::vector<Algorithm*> get_all_algorithms(
@@ -179,9 +263,6 @@ public:
             size_t workspace_limit_in_bytes =
                     std::numeric_limits<size_t>::max(),
             bool reproducible = false) = 0;
-
-protected:
-    ~MultiAlgoOpr() = default;
 };
 
 //! specializae for nargs == 4
@@ -189,6 +270,40 @@ template <class Opr>
 class MultiAlgoOpr<Opr, 4> : public MultiAlgoOpr<Opr, -1> {
 public:
     using Algorithm = detail::Algorithm;
+    using AlgorithmInfo = detail::Algorithm::Info;
+
+    //! get all possible algorithm decriptions for the specified layouts
+    std::vector<AlgorithmInfo> get_all_algorithms_info(const TensorLayout& p0,
+                                                       const TensorLayout& p1,
+                                                       const TensorLayout& p2,
+                                                       const TensorLayout& p3) {
+        std::vector<AlgorithmInfo> ret;
+        for (auto&& algo : get_all_algorithms(p0, p1, p2, p3)) {
+            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,
+            const TensorLayout& p2, const TensorLayout& p3,
+            size_t workspace_limit_in_bytes =
+                    std::numeric_limits<size_t>::max(),
+            bool reproducible = false) {
+        return get_algorithm_heuristic(p0, p1, p2, p3, workspace_limit_in_bytes,
+                                       reproducible)
+                ->info();
+    }
+
+protected:
+    ~MultiAlgoOpr() = default;
 
     //! get all possible algorithms for the specified layouts
     virtual std::vector<Algorithm*> get_all_algorithms(
@@ -207,9 +322,6 @@ public:
             size_t workspace_limit_in_bytes =
                     std::numeric_limits<size_t>::max(),
             bool reproducible = false) = 0;
-
-protected:
-    ~MultiAlgoOpr() = default;
 };
 
 //! specializae for nargs == 5
@@ -217,6 +329,42 @@ template <class Opr>
 class MultiAlgoOpr<Opr, 5> : public MultiAlgoOpr<Opr, -1> {
 public:
     using Algorithm = detail::Algorithm;
+    using AlgorithmInfo = detail::Algorithm::Info;
+
+    //! get all possible algorithm decriptions for the specified layouts
+    std::vector<AlgorithmInfo> get_all_algorithms_info(const TensorLayout& p0,
+                                                       const TensorLayout& p1,
+                                                       const TensorLayout& p2,
+                                                       const TensorLayout& p3,
+                                                       const TensorLayout& p4) {
+        std::vector<AlgorithmInfo> ret;
+        for (auto&& algo : get_all_algorithms(p0, p1, p2, p3, p4)) {
+            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,
+            const TensorLayout& p2, const TensorLayout& p3,
+            const TensorLayout& p4,
+            size_t workspace_limit_in_bytes =
+                    std::numeric_limits<size_t>::max(),
+            bool reproducible = false) {
+        return get_algorithm_heuristic(p0, p1, p2, p3, p4,
+                                       workspace_limit_in_bytes, reproducible)
+                ->info();
+    }
+
+protected:
+    ~MultiAlgoOpr() = default;
 
     //! get all possible algorithms for the specified layouts
     virtual std::vector<Algorithm*> get_all_algorithms(
@@ -237,9 +385,6 @@ public:
             size_t workspace_limit_in_bytes =
                     std::numeric_limits<size_t>::max(),
             bool reproducible = false) = 0;
-
-protected:
-    ~MultiAlgoOpr() = default;
 };
 
 //! specializae for nargs == 8
@@ -247,6 +392,42 @@ template <class Opr>
 class MultiAlgoOpr<Opr, 8> : public MultiAlgoOpr<Opr, -1> {
 public:
     using Algorithm = detail::Algorithm;
+    using AlgorithmInfo = detail::Algorithm::Info;
+
+    //! get all possible algorithm decriptions for the specified layouts
+    std::vector<AlgorithmInfo> get_all_algorithms_info(
+            const TensorLayout& p0, const TensorLayout& p1,
+            const TensorLayout& p2, const TensorLayout& p3,
+            const TensorLayout& p4, const TensorLayout& p5,
+            const TensorLayout& p6, const TensorLayout& p7) {
+        std::vector<AlgorithmInfo> ret;
+        for (auto&& algo : get_all_algorithms(p0, p1, p2, p3, p4, p5, p6, p7)) {
+            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
+     */
+    AlgorithmInfo get_algorithm_info_heuristic(
+            const TensorLayout& p0, const TensorLayout& p1,
+            const TensorLayout& p2, const TensorLayout& p3,
+            const TensorLayout& p4, const TensorLayout& p5,
+            const TensorLayout& p6, const TensorLayout& p7,
+            size_t workspace_limit_in_bytes =
+                    std::numeric_limits<size_t>::max(),
+            bool reproducible = false) {
+        return get_algorithm_heuristic(p0, p1, p2, p3, p4, p5, p6, p7,
+                                       workspace_limit_in_bytes, reproducible)
+                ->info();
+    }
+
+protected:
+    ~MultiAlgoOpr() = default;
 
     //! get all possible algorithms for the specified layouts
     virtual std::vector<Algorithm*> get_all_algorithms(
@@ -269,9 +450,6 @@ public:
             size_t workspace_limit_in_bytes =
                     std::numeric_limits<size_t>::max(),
             bool reproducible = false) = 0;
-
-protected:
-    ~MultiAlgoOpr() = default;
 };
 }  // namespace detail
 }  // namespace megdnn

dnn/src/aarch64/conv_bias/fp16/algos.h

@@ -31,6 +31,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::FLOAT16, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(AARCH64_DIRECT_STRD2_FP16)
 };
 }  // namespace aarch64
 }  // namespace megdnn

dnn/src/aarch64/conv_bias/fp32/algos.h

@@ -36,6 +36,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::FLOAT32, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(AARCH64_DIRECT_STRD2_FP32)
 };
 
 }  // namespace aarch64

dnn/src/aarch64/conv_bias/int8/algos.h

@@ -48,6 +48,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::QINT8X8X32, AlgoCategory::IM2COL};
     }
+    MEGDNN_DECL_ALGO_TYPE(AARCH64_MATMUL_S8)
 };
 
 }  // namespace aarch64

dnn/src/aarch64/conv_bias/opr_impl.cpp

@@ -32,28 +32,54 @@ class ConvBiasImpl::AlgoPack : NonCopyableObj {
     AlgoF16DirectStride2 f16_direct_stride2;
 #endif
 
+    fallback::ConvBiasImpl::AlgoBase::Mapper m_all_algos_map;
+    SmallVector<fallback::ConvBiasImpl::AlgoBase*> m_direct_algos;
+    SmallVector<fallback::ConvBiasImpl::AlgoBase*> m_matmul_algos;
+
 public:
     AlgoPack() {
-        matmul_algos.emplace_back(&qu8_matrix_mul);
-        matmul_algos.emplace_back(&s8_matrix_mul);
+        m_matmul_algos.emplace_back(&qu8_matrix_mul);
+        m_matmul_algos.emplace_back(&s8_matrix_mul);
 #if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-        direct_algos.emplace_back(&f16_direct_stride2);
+        m_direct_algos.emplace_back(&f16_direct_stride2);
 #endif
-        direct_algos.emplace_back(&f32_direct_stride2);
+        m_direct_algos.emplace_back(&f32_direct_stride2);
+
+        for (auto&& algo : m_direct_algos) {
+            m_all_algos_map.emplace(algo->info().desc, algo);
+        }
+        for (auto&& algo : m_matmul_algos) {
+            m_all_algos_map.emplace(algo->info().desc, algo);
+        }
+    }
+
+    const SmallVector<fallback::ConvBiasImpl::AlgoBase*>& direct_algos() const {
+        return m_direct_algos;
+    }
+    const SmallVector<fallback::ConvBiasImpl::AlgoBase*>& matmul_algos()
+            const {
+        return m_matmul_algos;
     }
-    SmallVector<AlgoBase*> direct_algos;
-    SmallVector<AlgoBase*> matmul_algos;
+    const AlgoBase::Mapper& all_algos_map() const { return m_all_algos_map; }
+
 };
 
-SmallVector<fallback::ConvBiasImpl::AlgoBase*> ConvBiasImpl::algo_pack() {
-    static AlgoPack sl_algo_pack;
-    auto&& algos = arm_common::ConvBiasImpl::algo_pack();
-    algos.insert(algos.begin(), sl_algo_pack.direct_algos.begin(),
-                 sl_algo_pack.direct_algos.end());
+const ConvBiasImpl::AlgoPack& ConvBiasImpl::algo_pack() {
+    static AlgoPack algo_pack;
+    return algo_pack;
+}
+
+MEGDNN_FB_DEF_GET_ALGO_FROM_DESC(ConvBiasImpl)
+
+SmallVector<fallback::ConvBiasImpl::AlgoBase*>
+ConvBiasImpl::get_all_packed_algo() {
+    auto&& algos = arm_common::ConvBiasImpl::get_all_packed_algo();
+    algos.insert(algos.begin(), algo_pack().direct_algos().begin(),
+                 algo_pack().direct_algos().end());
     //! We put matmul algos at the begin. Because matmul will get privilege when
     //! prefer return true. See
-    algos.insert(algos.begin(), sl_algo_pack.matmul_algos.begin(),
-                 sl_algo_pack.matmul_algos.end());
+    algos.insert(algos.begin(), algo_pack().matmul_algos().begin(),
+                 algo_pack().matmul_algos().end());
     return std::move(algos);
 }
 

dnn/src/aarch64/conv_bias/opr_impl.h

@@ -25,7 +25,9 @@ public:
         }
     };
 
-    SmallVector<fallback::ConvBiasImpl::AlgoBase*> algo_pack() override;
+    SmallVector<fallback::ConvBiasImpl::AlgoBase*> get_all_packed_algo() override;
+
+    MEGDNN_FB_DECL_GET_ALGO_FROM_DESC(ConvBiasImpl);
 
 protected:
     const char* get_algorithm_set_name() const override;
@@ -38,6 +40,7 @@ private:
     class AlgoF16DirectStride2;
 #endif
     class AlgoPack;
+    static const AlgoPack& algo_pack();
 };
 
 }  // namespace aarch64

dnn/src/aarch64/conv_bias/quint8/algos.h

@@ -48,6 +48,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::QUINT8X8X32, AlgoCategory::IM2COL};
     }
+    MEGDNN_DECL_ALGO_TYPE(AARCH64_MATMUL_QU8)
 };
 }  // namespace aarch64
 }  // namespace megdnn

dnn/src/aarch64/matrix_mul/algos.h

@@ -27,6 +27,7 @@ public:
     size_t get_workspace(const KernSizeParam&) const override;
     kern_t get_kern(const KernSizeParam&) const override;
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(AARCH64_F32_K8X12X1)
 };
 
 class MatrixMulImpl::AlgoF32MK4_8x12x1 final : public AlgoBase {
@@ -37,6 +38,7 @@ public:
     size_t get_workspace(const KernSizeParam&) const override;
     kern_t get_kern(const KernSizeParam&) const override;
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(AARCH64_F32_MK4_K8X12X1)
 };
 
 class MatrixMulImpl::AlgoF32K4x16x1 final : public AlgoBase {
@@ -47,6 +49,7 @@ public:
     size_t get_workspace(const KernSizeParam&) const override;
     kern_t get_kern(const KernSizeParam&) const override;
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(AARCH64_F32_K4X16X1)
 };
 
 class MatrixMulImpl::AlgoF32MK4_4x16 final : public AlgoBase {
@@ -58,10 +61,17 @@ public:
     kern_t get_kern(const KernSizeParam&) const override;
     PackMode packmode() const override { return PackMode::NO_PACK; }
     MEGDNN_OVERRIDE_MATMUL_DESC(4, 16, 4, 4, AlgoDataType::FLOAT32, MK4)
+    MEGDNN_DECL_ALGO_TYPE(AARCH64_F32_MK4_4x16)
 };
 
 class MatrixMulImpl::AlgoF32Gemv final
-        : public arm_common::MatrixMulImpl::AlgoF32Gemv {};
+        : public arm_common::MatrixMulImpl::AlgoF32Gemv {
+public:
+    AlgoF32Gemv() : arm_common::MatrixMulImpl::AlgoF32Gemv() {
+        m_handle_type = Handle::HandleType::AARCH64;
+    }
+    MEGDNN_DECL_ALGO_TYPE(AARCH64_F32_GEMV)
+};
 
 #if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
 class MatrixMulImpl::AlgoF16K8x24x1 final : public AlgoBase {
@@ -72,6 +82,7 @@ public:
     size_t get_workspace(const KernSizeParam&) const override;
     kern_t get_kern(const KernSizeParam&) const override;
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(AARCH64_F16_K8X24X1)
 };
 
 class MatrixMulImpl::AlgoF16MK8_8x8 final : public AlgoBase {
@@ -83,6 +94,7 @@ public:
     kern_t get_kern(const KernSizeParam&) const override;
     PackMode packmode() const override { return PackMode::NO_PACK; }
     MEGDNN_OVERRIDE_MATMUL_DESC(8, 8, 8, 2, AlgoDataType::FLOAT16, MK8)
+    MEGDNN_DECL_ALGO_TYPE(AARCH64_F16_MK8_8X8)
 };
 
 #endif
@@ -98,6 +110,7 @@ public:
     size_t get_workspace(const KernSizeParam&) const override;
     kern_t get_kern(const KernSizeParam&) const override;
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(AARCH64_INT8X8X32_K8X12X4_DOTPROD)
 };
 
 class MatrixMulImpl::AlgoInt8x8x32MK4_8x12x4DotProd final : public AlgoBase {
@@ -110,6 +123,7 @@ public:
     size_t get_workspace(const KernSizeParam&) const override;
     kern_t get_kern(const KernSizeParam&) const override;
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(AARCH64_INT8X8X32_MK4_8X12X4_DOTPROD)
 };
 #else
 
@@ -124,6 +138,7 @@ public:
     PackMode packmode() const override { return PackMode::DEFAULT; }
 
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(AARCH64_INT8X8X32_MK4_4X4X16)
 };
 
 class MatrixMulImpl::AlgoInt8x8x32K4x4x16 final : public AlgoBase {
@@ -136,6 +151,7 @@ public:
     kern_t get_kern(const KernSizeParam&) const override;
 
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(AARCH64_INT8X8X32_K4X4X16)
 };
 
 class MatrixMulImpl::AlgoInt8x8x32K8x8x8 final : public AlgoBase {
@@ -147,6 +163,7 @@ public:
     size_t get_workspace(const KernSizeParam&) const override;
     kern_t get_kern(const KernSizeParam&) const override;
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(AARCH64_INT8X8X32_K8X8X8)
 };
 #endif
 
@@ -160,6 +177,7 @@ public:
     kern_t get_kern(const KernSizeParam&) const override;
 
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(AARCH64_INT8X8X16_K8X8X8)
 };
 
 class MatrixMulImpl::AlgoInt8x8x16K4x4x16 final : public AlgoBase {
@@ -171,6 +189,7 @@ public:
     size_t get_workspace(const KernSizeParam&) const override;
     kern_t get_kern(const KernSizeParam&) const override;
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(AARCH64_INT8X8X16_K4X4X16)
 };
 
 class MatrixMulImpl::AlgoInt8x8x16MK4_16x12x4 final : public AlgoBase {
@@ -186,6 +205,7 @@ public:
     PackMode packmode() const override { return PackMode::DEFAULT; }
 
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(AARCH64_INT8X8X16_MK4_16X12X4)
 };
 
 class MatrixMulImpl::AlgoInt8x8x16MK4_K8x8x8 final : public AlgoBase {
@@ -201,6 +221,7 @@ public:
     PackMode packmode() const override { return PackMode::DEFAULT; }
 
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(AARCH64_INT8X8X16_MK4_K8X8X8)
 };
 
 class MatrixMulImpl::AlgoInt8x8x16MK4_4x4x8 final : public AlgoBase {
@@ -214,6 +235,7 @@ public:
     PackMode packmode() const override { return PackMode::DEFAULT; }
 
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(AARCH64_INT8X8X16_MK4_4X4X8)
 };
 
 class MatrixMulImpl::AlgoInt16x16x32K12x8x1 final : public AlgoBase {
@@ -225,6 +247,7 @@ public:
     size_t get_workspace(const KernSizeParam&) const override;
     kern_t get_kern(const KernSizeParam&) const override;
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(AARCH64_INT16X16X32_K12X8X1)
 };
 
 class MatrixMulImpl::AlgoInt16x16x32MK8_8x8 final : public AlgoBase {
@@ -236,6 +259,7 @@ public:
     kern_t get_kern(const KernSizeParam&) const override;
     PackMode packmode() const override { return PackMode::NO_PACK; }
     MEGDNN_OVERRIDE_MATMUL_DESC(8, 8, 8, 2, AlgoDataType::INT16X16X32, MK8)
+    MEGDNN_DECL_ALGO_TYPE(AARCH64_INT16X16X32_MK8_8X8)
 };
 
 #if __ARM_FEATURE_DOTPROD
@@ -249,6 +273,7 @@ public:
     size_t get_workspace(const KernSizeParam&) const override;
     kern_t get_kern(const KernSizeParam&) const override;
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(AARCH64_QUINT8_K8X8X4_DOTPROD)
 };
 
 class MatrixMulImpl::AlgoQuint8GemvDotProd final : public AlgoBase {
@@ -262,6 +287,7 @@ public:
     AlgoSet algoset() const override { return AlgoSet::ALGO_TYPE_GEMV; }
     PackMode packmode() const override { return PackMode::NO_PACK; }
     MEGDNN_OVERRIDE_MATMUL_DESC(8, 16, 1, 2, AlgoDataType::QUINT8X8X32, DEFAULT)
+    MEGDNN_DECL_ALGO_TYPE(AARCH64_QUINT8_GEMV_DOTPROD)
 };
 #else
 
@@ -273,6 +299,7 @@ public:
     size_t get_workspace(const KernSizeParam&) const override;
     kern_t get_kern(const KernSizeParam&) const override;
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(AARCH64_QUINT8_K8X8X8)
 };
 #endif
 

dnn/src/aarch64/matrix_mul/opr_impl.cpp

@@ -51,49 +51,66 @@ class MatrixMulImpl::AlgoPack : NonCopyableObj {
     AlgoQuint8K8x8x8 quint8_k8x8x8;
 #endif
 
+    SmallVector<fallback::MatrixMulImpl::AlgoBase*> m_all_algos;
+    fallback::MatrixMulImpl::AlgoBase::Mapper m_all_algos_map;
 public:
-    SmallVector<fallback::MatrixMulImpl::AlgoBase*> all_algos;
 
     AlgoPack() {
-        all_algos.emplace_back(&f32_gemv);
-        all_algos.emplace_back(&f32K8x12x1);
-        all_algos.emplace_back(&f32_mk4_8x12x1);
-        all_algos.emplace_back(&f32k4x16x1);
-        all_algos.emplace_back(&f32mk4_4x16);
+        m_all_algos.emplace_back(&f32_gemv);
+        m_all_algos.emplace_back(&f32K8x12x1);
+        m_all_algos.emplace_back(&f32_mk4_8x12x1);
+        m_all_algos.emplace_back(&f32k4x16x1);
+        m_all_algos.emplace_back(&f32mk4_4x16);
 #if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-        all_algos.emplace_back(&f16_k8x24x1);
-        all_algos.emplace_back(&f16_mk8_8x8);
+        m_all_algos.emplace_back(&f16_k8x24x1);
+        m_all_algos.emplace_back(&f16_mk8_8x8);
 #endif
 #if __ARM_FEATURE_DOTPROD
-        all_algos.emplace_back(&int8x8x32_k8x12x4_dotprod);
-        all_algos.emplace_back(&int8x8x32_mk4_8x12x4_dotprod);
+        m_all_algos.emplace_back(&int8x8x32_k8x12x4_dotprod);
+        m_all_algos.emplace_back(&int8x8x32_mk4_8x12x4_dotprod);
 #else
-        all_algos.emplace_back(&int8x8x32_k4x4x16);
-        all_algos.emplace_back(&int8x8x32_k8x8x8);
-        all_algos.emplace_back(&int8x8x32_mk4_4x4x16);
+        m_all_algos.emplace_back(&int8x8x32_k4x4x16);
+        m_all_algos.emplace_back(&int8x8x32_k8x8x8);
+        m_all_algos.emplace_back(&int8x8x32_mk4_4x4x16);
 #endif
-        all_algos.emplace_back(&int8x8x16_k4x4x16);
-        all_algos.emplace_back(&int8x8x16_k8x8x8);
-        all_algos.emplace_back(&int8x8x16_mk4_k8x8x8);
-        all_algos.emplace_back(&int8x8x16_mk4_4x4x8);
-        all_algos.emplace_back(&int8x8x16_mk4_16x12x4);
+        m_all_algos.emplace_back(&int8x8x16_k4x4x16);
+        m_all_algos.emplace_back(&int8x8x16_k8x8x8);
+        m_all_algos.emplace_back(&int8x8x16_mk4_k8x8x8);
+        m_all_algos.emplace_back(&int8x8x16_mk4_4x4x8);
+        m_all_algos.emplace_back(&int8x8x16_mk4_16x12x4);
 
-        all_algos.emplace_back(&int16x16x32_k12x8x1);
-        all_algos.emplace_back(&int16x16x32_mk8_8x8);
+        m_all_algos.emplace_back(&int16x16x32_k12x8x1);
+        m_all_algos.emplace_back(&int16x16x32_mk8_8x8);
 #if __ARM_FEATURE_DOTPROD
-        all_algos.emplace_back(&quint8_gemv_dotprod);
-        all_algos.emplace_back(&quint8_k8x8x4_dotprod);
+        m_all_algos.emplace_back(&quint8_gemv_dotprod);
+        m_all_algos.emplace_back(&quint8_k8x8x4_dotprod);
 #else
-        all_algos.emplace_back(&quint8_k8x8x8);
+        m_all_algos.emplace_back(&quint8_k8x8x8);
 #endif
+
+        for (auto&& algo : m_all_algos) {
+            m_all_algos_map.emplace(algo->info().desc, algo);
+        }
+    }
+
+    const SmallVector<fallback::MatrixMulImpl::AlgoBase*>& all_algos() const {
+        return m_all_algos;
     }
+    const AlgoBase::Mapper& all_algos_map() const { return m_all_algos_map; }
 };
 
-SmallVector<fallback::MatrixMulImpl::AlgoBase*> MatrixMulImpl::algo_pack() {
-    static AlgoPack s_algo_pack;
-    auto&& algos = arm_common::MatrixMulImpl::algo_pack();
-    algos.insert(algos.begin(), s_algo_pack.all_algos.begin(),
-                 s_algo_pack.all_algos.end());
+const MatrixMulImpl::AlgoPack& MatrixMulImpl::algo_pack() {
+    static AlgoPack algo_pack;
+    return algo_pack;
+}
+
+MEGDNN_FB_DEF_GET_ALGO_FROM_DESC(MatrixMulImpl)
+
+SmallVector<fallback::MatrixMulImpl::AlgoBase*>
+MatrixMulImpl::get_all_packed_algo() {
+    auto&& algos = arm_common::MatrixMulImpl::get_all_packed_algo();
+    algos.insert(algos.begin(), algo_pack().all_algos().begin(),
+                 algo_pack().all_algos().end());
     return std::move(algos);
 }
 

dnn/src/aarch64/matrix_mul/opr_impl.h

@@ -25,7 +25,10 @@ public:
         }
     };
 
-    SmallVector<fallback::MatrixMulImpl::AlgoBase*> algo_pack() override;
+    SmallVector<fallback::MatrixMulImpl::AlgoBase*> get_all_packed_algo()
+            override;
+
+    MEGDNN_FB_DECL_GET_ALGO_FROM_DESC(MatrixMulImpl);
 
 private:
     class AlgoF32K8x12x1;     // Aarch64 F32 Kernel 8X12X1
@@ -66,6 +69,8 @@ private:
     class AlgoInt8x8x16MK4_K8x8x8;  // Aarch64 Int4x4x16 Kernel 4x4x16
 
     class AlgoPack;
+public:
+    static const AlgoPack& algo_pack();
 };
 
 }  // namespace aarch64

dnn/src/arm_common/conv_bias/f16/algos.h

@@ -30,6 +30,7 @@ public:
         return m_name.c_str();
     }
     MEGDNN_WINOGRAD_ALGO_FUN_DECLARE(AlgoDataType::FLOAT16);
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_WINOGRAD_F23_FP16)
 };
 
 class ConvBiasImpl::AlgoFP16WinogradF45 final : public AlgoBase {
@@ -45,7 +46,7 @@ public:
         return m_name.c_str();
     }
     MEGDNN_WINOGRAD_ALGO_FUN_DECLARE(AlgoDataType::FLOAT16);
-
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_WINOGRAD_F45_FP16)
 };
 class ConvBiasImpl::AlgoFP16WinogradF63 final : public AlgoBase {
 public:
@@ -61,6 +62,7 @@ public:
     }
 
     MEGDNN_WINOGRAD_ALGO_FUN_DECLARE(AlgoDataType::FLOAT16);
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_WINOGRAD_F63_FP16)
 };
 class ConvBiasImpl::AlgoFP16WinogradF23_8x8 final : public AlgoBase {
 public:
@@ -75,6 +77,7 @@ public:
         return m_name.c_str();
     }
     MEGDNN_WINOGRAD_ALGO_FUN_DECLARE(AlgoDataType::FLOAT16);
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_WINOGRAD_F23_8X8_FP16)
 };
 
 class ConvBiasImpl::AlgoF16Direct final : public AlgoBase {
@@ -94,6 +97,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override{
         return {AlgoDataType::FLOAT16, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_FP16)
 };
 
 class ConvBiasImpl::AlgoF16DirectStride1 final : public AlgoBase {
@@ -110,6 +114,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::FLOAT16, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_STRD1_FP16)
 };
 
 }  // namespace arm_common

dnn/src/arm_common/conv_bias/fp32/algos.h

@@ -30,6 +30,7 @@ public:
         return m_name.c_str();
     }
     MEGDNN_WINOGRAD_ALGO_FUN_DECLARE(AlgoDataType::FLOAT32);
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_WINOGRAD_F23_4X4_FP32)
 };
 
 class ConvBiasImpl::AlgoFP32WinogradF63 final : public AlgoBase {
@@ -45,6 +46,7 @@ public:
         return m_name.c_str();
     }
     MEGDNN_WINOGRAD_ALGO_FUN_DECLARE(AlgoDataType::FLOAT32);
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_WINOGRAD_F63_FP32)
 };
 
 class ConvBiasImpl::AlgoFP32WinogradF63_4x4 final : public AlgoBase {
@@ -60,6 +62,7 @@ public:
         return m_name.c_str();
     }
     MEGDNN_WINOGRAD_ALGO_FUN_DECLARE(AlgoDataType::FLOAT32);
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_WINOGRAD_F63_4X4_FP32)
 };
 
 class ConvBiasImpl::AlgoFP32WinogradF54 final : public AlgoBase {
@@ -75,6 +78,7 @@ public:
         return m_name.c_str();
     }
     MEGDNN_WINOGRAD_ALGO_FUN_DECLARE(AlgoDataType::FLOAT32);
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_WINOGRAD_F54_FP32)
 };
 
 class ConvBiasImpl::AlgoFP32WinogradF45 final : public AlgoBase {
@@ -90,6 +94,7 @@ public:
         return m_name.c_str();
     }
     MEGDNN_WINOGRAD_ALGO_FUN_DECLARE(AlgoDataType::FLOAT32);
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_WINOGRAD_F45_FP32)
 };
 
 //===================== NCHW44 Winograd Support =====================//
@@ -107,6 +112,7 @@ public:
         return m_name.c_str();
     }
     MEGDNN_WINOGRAD_ALGO_FUN_DECLARE(AlgoDataType::FLOAT32);
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_WINOGRAD_F23_4X4_NCHW44_F32)
 };
 
 class ConvBiasImpl::AlgoFP32WinogradF63_4x4_NCHW44 final : public AlgoBase {
@@ -123,6 +129,7 @@ public:
         return m_name.c_str();
     }
     MEGDNN_WINOGRAD_ALGO_FUN_DECLARE(AlgoDataType::FLOAT32);
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_WINOGRAD_F63_4X4_NCHW44_F32)
 };
 
 class ConvBiasImpl::AlgoFP32WinogradF73_4x4_NCHW44 final : public AlgoBase {
@@ -139,6 +146,7 @@ public:
         return m_name.c_str();
     }
     MEGDNN_WINOGRAD_ALGO_FUN_DECLARE(AlgoDataType::FLOAT32);
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_WINOGRAD_F73_4X4_NCHW44_F32)
 };
 // ================================================================= //
 
@@ -157,6 +165,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::FLOAT32, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_FP32)
 };
 
 class ConvBiasImpl::AlgoF32DirectStride1 final : public AlgoBase {
@@ -174,6 +183,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::FLOAT32, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_STRD1_FP32)
 };
 
 class ConvBiasImpl::AlgoF32DirectStride2 final : public AlgoBase {
@@ -191,6 +201,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::FLOAT32, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_STRD2_FP32)
 };
 
 class ConvBiasImpl::AlgoF32DirectNCHW44 final : public AlgoBase {
@@ -209,6 +220,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::FLOAT32, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_NCHW44_FP32)
 };
 
 class ConvBiasImpl::AlgoF32DirectNCHWNCHW44 final : public AlgoBase {
@@ -227,6 +239,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::FLOAT32, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_NCHW_NCHW44_FP32)
 };
 
 class ConvBiasImpl::AlgoF32ChannelWiseNCHW44 final : public AlgoBase {
@@ -244,6 +257,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::FLOAT32, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_CHWNWISE_NCHW44_F32)
 };
 
 }  // namespace arm_common

dnn/src/arm_common/conv_bias/int8/algos.h

@@ -33,6 +33,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::QINT8X8X32, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_STRD1_S8)
 };
 
 class ConvBiasImpl::AlgoS8DirectStride2 final : public AlgoBase {
@@ -49,6 +50,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::QINT8X8X32, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_STRD2_S8)
 };
 
 class ConvBiasImpl::AlgoS8DirectNCHW44 final : public AlgoBase {
@@ -65,6 +67,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::QINT8X8X32, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_NCHW44)
 };
 
 class ConvBiasImpl::AlgoS8DirectNCHWNCHW44 final : public AlgoBase {
@@ -81,6 +84,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::QINT8X8X32, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_NCHW_NCHW44_S8)
 };
 
 class ConvBiasImpl::AlgoS8ChanWiseStride1NCHW44 final : public AlgoBase {
@@ -95,6 +99,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::QINT8X8X32, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_CHANWISE_STRD1_NCHW44_S8)
 };
 
 class ConvBiasImpl::AlgoS8ChanWiseStride2NCHW44 final : public AlgoBase {
@@ -109,6 +114,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::QINT8X8X32, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_CHANWISE_STRD2_NCHW44_S8)
 };
 
 #if __ARM_FEATURE_DOTPROD
@@ -126,6 +132,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::QINT8X8X32, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_NCHW_NCHW44_DOT_S8)
 };
 
 class ConvBiasImpl::AlgoDotS8DirectStride1 final : public AlgoBase {
@@ -142,6 +149,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::QINT8X8X32, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_STRD1_DOT_S8)
 };
 
 class ConvBiasImpl::AlgoDotS8DirectStride2 final : public AlgoBase {
@@ -159,6 +167,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::QINT8X8X32, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_STRD2_DOT_S8)
 };
 
 class ConvBiasImpl::AlgoDotS8Direct_NCHW44 final : public AlgoBase {
@@ -180,6 +189,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::QINT8X8X32, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_NCHW44_DOT_S8)
 };
 #endif
 
@@ -196,6 +206,7 @@ public:
         return m_name.c_str();
     }
     MEGDNN_WINOGRAD_ALGO_FUN_DECLARE(AlgoDataType::QINT8X8X32);
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_WINOGRAD_F23_8X8_S8)
 };
 
 //=======================input int8 compute fp32 output int8============
@@ -213,6 +224,7 @@ public:
         return m_name.c_str();
     }
     MEGDNN_WINOGRAD_ALGO_FUN_DECLARE(AlgoDataType::QINT8X8X32);
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_WINOGRAD_F23_8X8_NCHW44_S8CF32)
 };
 
 //=======================input int8 compute int16 output int8============
@@ -231,6 +243,7 @@ public:
     }
 
     MEGDNN_WINOGRAD_ALGO_FUN_DECLARE(AlgoDataType::QINT8X8X32);
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_WINOGRAD_F23_8X8_NCHW44_S8)
 };
 
 }  // namespace arm_common

dnn/src/arm_common/conv_bias/int8x8x16/algos.h

@@ -39,6 +39,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::INT8X8X16, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_INT8X8X16)
 };
 
 class ConvBiasImpl::AlgoS8x8x16DirectNCHW44 final : public AlgoBase {
@@ -54,6 +55,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::INT8X8X16, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_NCHW44_INT8X8X16)
 };
 
 class ConvBiasImpl::AlgoI8x8x16Stride2 final : public AlgoBase {
@@ -80,6 +82,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::INT8X8X16, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_STRD2_INT8X8X16)
 };
 
 class ConvBiasImpl::AlgoI8x8x16Stride2Filter2 final : public AlgoBase {
@@ -96,12 +99,16 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::INT8X8X16, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_STRD2_F2_INT8X8X16)
 };
 
-class ConvBiasImpl::AlgoS8x8x16ChanWiseStride1Stride2NCHW44 final : public AlgoBase {
+class ConvBiasImpl::AlgoS8x8x16ChanWiseStride1Stride2NCHW44 final
+        : public AlgoBase {
 public:
     bool is_reproducible() const override { return true; }
-    const char* name() const override { return "S8x8x16_CHAN_WISE_STRD1_STRD2_NCHW44"; }
+    const char* name() const override {
+        return "S8x8x16_CHAN_WISE_STRD1_STRD2_NCHW44";
+    }
     bool usable(const NCBKernSizeParam& param,
                 AlgoSelectionStrategy algo_selection_strategy) const override;
     size_t get_workspace(
@@ -111,6 +118,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::INT8X8X16, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_CHWNWISE_STRD1_STRD2_NCHW44_INT8X8X16)
 };
 
 class ConvBiasImpl::AlgoI8x8x16DirectNCHWNCHW44 final : public AlgoBase {
@@ -129,6 +137,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::INT8X8X16, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_NCHW_NCHW44_INT8X8X16)
 };
 
 }  // namespace arm_common

dnn/src/arm_common/conv_bias/opr_impl.cpp

@@ -88,46 +88,50 @@ class ConvBiasImpl::AlgoPack : NonCopyableObj {
 #endif
 
     SmallVector<std::unique_ptr<AlgoBase>> refhold;
+    fallback::ConvBiasImpl::AlgoBase::Mapper m_all_algos_map;
+    SmallVector<fallback::ConvBiasImpl::AlgoBase*> m_direct_algos;
+    SmallVector<fallback::ConvBiasImpl::AlgoBase*> m_winograd_algos;
 
 public:
     AlgoPack() {
 #if __ARM_FEATURE_DOTPROD
-        direct_algos.emplace_back(&ds8_direct_stride1);
-        direct_algos.emplace_back(&ds8_direct_stride2);
-        direct_algos.emplace_back(&du8_direct_stride1);
-        direct_algos.emplace_back(&du8_direct_stride2);
+        m_direct_algos.emplace_back(&ds8_direct_stride1);
+        m_direct_algos.emplace_back(&ds8_direct_stride2);
+        m_direct_algos.emplace_back(&du8_direct_stride1);
+        m_direct_algos.emplace_back(&du8_direct_stride2);
 
-        direct_algos.emplace_back(&ds8_direct_nchw44);
-        direct_algos.emplace_back(&ds8_direct_nchw_nchw44);
+        m_direct_algos.emplace_back(&ds8_direct_nchw44);
+        m_direct_algos.emplace_back(&ds8_direct_nchw_nchw44);
 #endif
-        direct_algos.emplace_back(&qu8_direct_stride2);
-        direct_algos.emplace_back(&qu8_direct_stride1);
-        direct_algos.emplace_back(&s8_direct_stride2);
-        direct_algos.emplace_back(&s8_direct_nchw44);
-        direct_algos.emplace_back(&s8x8x16_direct_nchw44);
-        direct_algos.emplace_back(&s8_direct_nchw_nchw44);
-        direct_algos.emplace_back(&s8_direct_stride1);
-
-        direct_algos.emplace_back(&s8x8x16_channel_wise_stride1_stride2_nchw44);
-        direct_algos.emplace_back(&s8_channel_wise_stride1_nchw44);
-        direct_algos.emplace_back(&s8_channel_wise_stride2_nchw44);
+        m_direct_algos.emplace_back(&qu8_direct_stride2);
+        m_direct_algos.emplace_back(&qu8_direct_stride1);
+        m_direct_algos.emplace_back(&s8_direct_stride2);
+        m_direct_algos.emplace_back(&s8_direct_nchw44);
+        m_direct_algos.emplace_back(&s8x8x16_direct_nchw44);
+        m_direct_algos.emplace_back(&s8_direct_nchw_nchw44);
+        m_direct_algos.emplace_back(&s8_direct_stride1);
+
+        m_direct_algos.emplace_back(
+                &s8x8x16_channel_wise_stride1_stride2_nchw44);
+        m_direct_algos.emplace_back(&s8_channel_wise_stride1_nchw44);
+        m_direct_algos.emplace_back(&s8_channel_wise_stride2_nchw44);
 
 #if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-        direct_algos.emplace_back(&f16_direct_stride1);
-        direct_algos.emplace_back(&f16_direct);
+        m_direct_algos.emplace_back(&f16_direct_stride1);
+        m_direct_algos.emplace_back(&f16_direct);
 #endif
-        direct_algos.emplace_back(&i8x8x16_direct);
-        direct_algos.emplace_back(&i8x8x16_stride2_filter2);
-        direct_algos.emplace_back(&i8x8x16_stride2);
-        direct_algos.emplace_back(&i8x8x16_nchw_nchw44);
+        m_direct_algos.emplace_back(&i8x8x16_direct);
+        m_direct_algos.emplace_back(&i8x8x16_stride2_filter2);
+        m_direct_algos.emplace_back(&i8x8x16_stride2);
+        m_direct_algos.emplace_back(&i8x8x16_nchw_nchw44);
 
-        direct_algos.emplace_back(&f32_direct_stride2_nchw_nchw44);
-        direct_algos.emplace_back(&f32_chanel_wise_nchw44);
-        direct_algos.emplace_back(&f32_direct_nchw44);
+        m_direct_algos.emplace_back(&f32_direct_stride2_nchw_nchw44);
+        m_direct_algos.emplace_back(&f32_chanel_wise_nchw44);
+        m_direct_algos.emplace_back(&f32_direct_nchw44);
 
-        direct_algos.emplace_back(&f32_direct_stride1);
-        direct_algos.emplace_back(&f32_direct_stride2);
-        direct_algos.emplace_back(&f32_direct);
+        m_direct_algos.emplace_back(&f32_direct_stride1);
+        m_direct_algos.emplace_back(&f32_direct_stride2);
+        m_direct_algos.emplace_back(&f32_direct);
 
         static CpuOprDelegationStorage<2> storage;
         auto matmul_opr = storage.get<MatrixMul, 0>();
@@ -143,31 +147,31 @@ public:
                 refhold.emplace_back(new AlgoFP32WinogradF23_4x4(
                         static_cast<fallback::MatrixMulImpl::AlgoBase*>(algo),
                         tile_size));
-                winograd_algos.emplace_back(refhold.back().get());
+                m_winograd_algos.emplace_back(refhold.back().get());
                 refhold.emplace_back(new AlgoFP32WinogradF63_4x4(
                         static_cast<fallback::MatrixMulImpl::AlgoBase*>(algo),
                         tile_size));
-                winograd_algos.emplace_back(refhold.back().get());
+                m_winograd_algos.emplace_back(refhold.back().get());
                 refhold.emplace_back(new AlgoFP32WinogradF63_4x4_NCHW44(
                         static_cast<fallback::MatrixMulImpl::AlgoBase*>(algo),
                         tile_size));
-                winograd_algos.emplace_back(refhold.back().get());
+                m_winograd_algos.emplace_back(refhold.back().get());
                 refhold.emplace_back(new AlgoFP32WinogradF23_4x4_NCHW44(
                         static_cast<fallback::MatrixMulImpl::AlgoBase*>(algo),
                         tile_size));
-                winograd_algos.emplace_back(refhold.back().get());
+                m_winograd_algos.emplace_back(refhold.back().get());
 //! uncomment this when low precision mode is done
 #if 0
                 refhold.emplace_back(new AlgoFP32WinogradF73_4x4_NCHW44(
                         static_cast<fallback::MatrixMulImpl::AlgoBase*>(algo),
                         tile_size));
-                winograd_algos.emplace_back(refhold.back().get());
+                m_winograd_algos.emplace_back(refhold.back().get());
 #endif
                 //! Qint8x8x32 winograd compute with fp32
                 refhold.emplace_back(new AlgoS8CF32WinogradF23_4x4_NCHW44(
                         static_cast<fallback::MatrixMulImpl::AlgoBase*>(algo),
                         tile_size));
-                winograd_algos.emplace_back(refhold.back().get());
+                m_winograd_algos.emplace_back(refhold.back().get());
             }
         }
         matmul_algos = static_cast<arm_common::MatrixMulImpl*>(matmul_opr)
@@ -180,15 +184,15 @@ public:
                 refhold.emplace_back(new AlgoFP32WinogradF63(
                         static_cast<fallback::MatrixMulImpl::AlgoBase*>(algo),
                         tile_size));
-                winograd_algos.emplace_back(refhold.back().get());
+                m_winograd_algos.emplace_back(refhold.back().get());
                 refhold.emplace_back(new AlgoFP32WinogradF54(
                         static_cast<fallback::MatrixMulImpl::AlgoBase*>(algo),
                         tile_size));
-                winograd_algos.emplace_back(refhold.back().get());
+                m_winograd_algos.emplace_back(refhold.back().get());
                 refhold.emplace_back(new AlgoFP32WinogradF45(
                         static_cast<fallback::MatrixMulImpl::AlgoBase*>(algo),
                         tile_size));
-                winograd_algos.emplace_back(refhold.back().get());
+                m_winograd_algos.emplace_back(refhold.back().get());
             }
         }
 
@@ -203,15 +207,15 @@ public:
                 refhold.emplace_back(new AlgoFP16WinogradF23(
                         static_cast<fallback::MatrixMulImpl::AlgoBase*>(algo),
                         tile_size));
-                winograd_algos.emplace_back(refhold.back().get());
+                m_winograd_algos.emplace_back(refhold.back().get());
                 refhold.emplace_back(new AlgoFP16WinogradF45(
                         static_cast<fallback::MatrixMulImpl::AlgoBase*>(algo),
                         tile_size));
-                winograd_algos.emplace_back(refhold.back().get());
+                m_winograd_algos.emplace_back(refhold.back().get());
                 refhold.emplace_back(new AlgoFP16WinogradF63(
                         static_cast<fallback::MatrixMulImpl::AlgoBase*>(algo),
                         tile_size));
-                winograd_algos.emplace_back(refhold.back().get());
+                m_winograd_algos.emplace_back(refhold.back().get());
             }
         }
         matmul_algos = static_cast<arm_common::MatrixMulImpl*>(matmul_opr)
@@ -224,7 +228,7 @@ public:
                 refhold.emplace_back(new AlgoFP16WinogradF23_8x8(
                         static_cast<fallback::MatrixMulImpl::AlgoBase*>(algo),
                         tile_size));
-                winograd_algos.emplace_back(refhold.back().get());
+                m_winograd_algos.emplace_back(refhold.back().get());
             }
         }
 #endif
@@ -238,25 +242,48 @@ public:
                 refhold.emplace_back(new AlgoS8WinogradF23_8x8(
                         static_cast<fallback::MatrixMulImpl::AlgoBase*>(algo),
                         tile_size));
-                winograd_algos.emplace_back(refhold.back().get());
+                m_winograd_algos.emplace_back(refhold.back().get());
                 refhold.emplace_back(new AlgoS8WinogradF23_8x8_NCHW44(
                         static_cast<fallback::MatrixMulImpl::AlgoBase*>(algo),
                         tile_size));
-                winograd_algos.emplace_back(refhold.back().get());
+                m_winograd_algos.emplace_back(refhold.back().get());
             }
         }
+
+
+        for (auto&& algo : m_direct_algos) {
+            m_all_algos_map.emplace(algo->info().desc, algo);
+        }
+        for (auto&& algo : m_winograd_algos) {
+            m_all_algos_map.emplace(algo->info().desc, algo);
+        }
     }
-    SmallVector<AlgoBase*> direct_algos;
-    SmallVector<AlgoBase*> winograd_algos;
+
+    const SmallVector<fallback::ConvBiasImpl::AlgoBase*>& direct_algos()
+            const {
+        return m_direct_algos;
+    }
+    const SmallVector<fallback::ConvBiasImpl::AlgoBase*>& winograd_algos()
+            const {
+        return m_winograd_algos;
+    }
+    const AlgoBase::Mapper& all_algos_map() const { return m_all_algos_map; }
 };
 
-SmallVector<fallback::ConvBiasImpl::AlgoBase*> ConvBiasImpl::algo_pack() {
-    static AlgoPack sl_algo_pack;
-    auto&& algos = fallback::ConvBiasImpl::algo_pack();
-    algos.insert(algos.begin(), sl_algo_pack.direct_algos.begin(),
-                 sl_algo_pack.direct_algos.end());
-    algos.insert(algos.end(), sl_algo_pack.winograd_algos.begin(),
-                 sl_algo_pack.winograd_algos.end());
+const ConvBiasImpl::AlgoPack& ConvBiasImpl::algo_pack() {
+    static AlgoPack algo_pack;
+    return algo_pack;
+}
+
+MEGDNN_FB_DEF_GET_ALGO_FROM_DESC(ConvBiasImpl)
+
+SmallVector<fallback::ConvBiasImpl::AlgoBase*>
+ConvBiasImpl::get_all_packed_algo() {
+    auto&& algos = fallback::ConvBiasImpl::get_all_packed_algo();
+    algos.insert(algos.begin(), algo_pack().direct_algos().begin(),
+                 algo_pack().direct_algos().end());
+    algos.insert(algos.end(), algo_pack().winograd_algos().begin(),
+                 algo_pack().winograd_algos().end());
     return std::move(algos);
 }
 

dnn/src/arm_common/conv_bias/opr_impl.h

@@ -12,6 +12,7 @@
 #pragma once
 #include "src/common/utils.h"
 #include "src/fallback/conv_bias/opr_impl.h"
+#include "src/common/algo_base.h"
 
 namespace megdnn {
 namespace arm_common {
@@ -27,7 +28,7 @@ public:
         }
     };
 
-    SmallVector<fallback::ConvBiasImpl::AlgoBase*> algo_pack() override;
+    SmallVector<fallback::ConvBiasImpl::AlgoBase*> get_all_packed_algo() override;
 
     bool is_matmul_quantized_prefer(
             const fallback::ConvBiasImpl::NCBKernSizeParam& ncb_param)
@@ -35,7 +36,8 @@ public:
 
     SmallVector<AlgoCategory> suggest_algo_category_order(
             const NCBKernSizeParam& param) const override;
-    class AlgoPack;
+
+    MEGDNN_FB_DECL_GET_ALGO_FROM_DESC(ConvBiasImpl);
 
 protected:
     const char* get_algorithm_set_name() const override;
@@ -95,6 +97,9 @@ private:
     class AlgoF16Direct;
     class AlgoF16DirectStride1;
 #endif
+
+    class AlgoPack;
+    static const AlgoPack& algo_pack();
 };
 
 }  // namespace arm_common

dnn/src/arm_common/conv_bias/quint8/algos.h

@@ -32,6 +32,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::QUINT8X8X32, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_STRD1_QU8)
 };
 
 class ConvBiasImpl::AlgoQU8DirectStride2 final : public AlgoBase {
@@ -48,6 +49,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::QUINT8X8X32, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_STRD2_QU8)
 };
 #if __ARM_FEATURE_DOTPROD
 class ConvBiasImpl::AlgoDotU8DirectStride1 final : public AlgoBase {
@@ -65,6 +67,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::QUINT8X8X32, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_STRD1_DOT_QU8)
 };
 
 class ConvBiasImpl::AlgoDotU8DirectStride2 final : public AlgoBase {
@@ -81,6 +84,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::QUINT8X8X32, AlgoCategory::DIRECT};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_STRD2_DOT_QU8)
 };
 #endif
 }  // namespace arm_common

dnn/src/arm_common/convolution/int8x8x32/algos.h

@@ -36,6 +36,7 @@ public:
 
     ncb_kern_t dispatch_kern(fallback::ConvolutionBackwardDataImpl*,
                              const NCBKernSizeParam&) const override;
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_STRD1_DOT_INT8X8X32)
 };
 
 class ConvolutionBackwardDataImpl::AlgoSdot8DirectStride2 final
@@ -54,6 +55,7 @@ public:
 
     ncb_kern_t dispatch_kern(fallback::ConvolutionBackwardDataImpl*,
                              const NCBKernSizeParam&) const override;
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_STRD2_DOT_INT8X8X32)
 };
 
 #endif

dnn/src/arm_common/convolution/int8x8x32/conv_backdata_stride1.cpp

@@ -1086,6 +1086,10 @@ bool deconv::can_stride1_int8x8x32_dot(const NCBKernSizeParam& param) {
                     (FH == 2 || FH == 3 || FH == 5 || FH == 7) &&
                     FH >= PH + 1 && FW >= PW + 1;
 
+    avaiable &= (param.filter_type.enumv() == DTypeEnum::QuantizedS8 ||
+                 param.filter_type.enumv() == DTypeEnum::Int8) &&
+                (param.grad_type.enumv() == DTypeEnum::QuantizedS32 ||
+                 param.grad_type.enumv() == DTypeEnum::Int32);
     return avaiable &&
            ((FH == 2 && OC <= 8) ||
             ((FH == 3 || FH == 5 || FH == 7) && (IC < 32 && OC <= 16)));

dnn/src/arm_common/convolution/int8x8x32/conv_backdata_stride2.cpp

@@ -1180,6 +1180,10 @@ bool deconv::can_stride2_int8x8x32_dot(const NCBKernSizeParam& param) {
                     (FH == 2 || FH == 3 || FH == 5 || FH == 7) &&
                     FH >= PH + 1 && FW >= PW + 1;
 
+    avaiable &= (param.filter_type.enumv() == DTypeEnum::QuantizedS8 ||
+                 param.filter_type.enumv() == DTypeEnum::Int8) &&
+                (param.grad_type.enumv() == DTypeEnum::QuantizedS32 ||
+                 param.grad_type.enumv() == DTypeEnum::Int32);
     return avaiable && ((FH == 2 && OC <= 4) || (FH == 3 && OC <= 8) ||
                         (FH == 5 && OC <= 16) || (FH == 7 && OC < 32));
 }

dnn/src/arm_common/convolution/opr_impl.cpp

@@ -23,15 +23,54 @@ using namespace arm_common;
 
 
 /* ===================== ConvolutionBackwardData ===================== */
-struct ConvolutionBackwardDataImpl::AlgoPack {
+class ConvolutionBackwardDataImpl::AlgoPack : NonCopyableObj {
 #if __ARM_FEATURE_DOTPROD
     AlgoSdot8DirectStride1 i8x8x32_direct_stride1_sdot;
     AlgoSdot8DirectStride2 i8x8x32_direct_stride2_sdot;
     AlgoUdot8DirectStride1 quint8_direct_stride1_udot;
     AlgoUdot8DirectStride2 quint8_direct_stride2_udot;
 #endif
+
+    fallback::ConvolutionBackwardDataImpl::AlgoBase::Mapper m_all_algos_map;
+    SmallVector<fallback::ConvolutionBackwardDataImpl::AlgoBase*>
+            m_all_algos;
+
+public:
+    AlgoPack() {
+#if __ARM_FEATURE_DOTPROD
+        m_all_algos.emplace_back(&i8x8x32_direct_stride1_sdot);
+        m_all_algos.emplace_back(&i8x8x32_direct_stride2_sdot);
+        m_all_algos.emplace_back(&quint8_direct_stride1_udot);
+        m_all_algos.emplace_back(&quint8_direct_stride2_udot);
+#endif
+
+        for (auto&& algo : m_all_algos) {
+            m_all_algos_map.emplace(algo->info().desc, algo);
+        }
+    }
+
+    const SmallVector<fallback::ConvolutionBackwardDataImpl::AlgoBase*>&
+    all_algos() const {
+        return m_all_algos;
+    }
+    const AlgoBase::Mapper& all_algos_map() const { return m_all_algos_map; }
 };
-ConvolutionBackwardDataImpl::AlgoPack ConvolutionBackwardDataImpl::sm_algo_pack;
+
+const ConvolutionBackwardDataImpl::AlgoPack&
+ConvolutionBackwardDataImpl::algo_pack() {
+    static AlgoPack algo_pack;
+    return algo_pack;
+}
+
+MEGDNN_FB_DEF_GET_ALGO_FROM_DESC(ConvolutionBackwardDataImpl)
+
+SmallVector<fallback::ConvolutionBackwardDataImpl::AlgoBase*>
+ConvolutionBackwardDataImpl::get_all_packed_algo() {
+    auto&& algos = fallback::ConvolutionBackwardDataImpl::get_all_packed_algo();
+    algos.insert(algos.begin(), algo_pack().all_algos().begin(),
+                 algo_pack().all_algos().end());
+    return std::move(algos);
+}
 
 ConvolutionBackwardDataImpl::ncb_kern_t
 ConvolutionBackwardDataImpl::ncb_1g_dispatch_kern(
@@ -52,35 +91,6 @@ size_t ConvolutionBackwardDataImpl::ncb_1g_get_workspace(
                                                                        param);
 }
 
-std::vector<ConvolutionBackwardDataImpl::Algorithm*>
-ConvolutionBackwardDataImpl::ncb_1g_get_all_algorithms(
-        const NCBKernSizeParam& param) {
-    auto ret = fallback::ConvolutionBackwardDataImpl::ncb_1g_get_all_algorithms(
-            param);
-
-#if __ARM_FEATURE_DOTPROD
-    if ((param.filter_type.enumv() == DTypeEnum::QuantizedS8 ||
-         param.filter_type.enumv() == DTypeEnum::Int8) &&
-        (param.grad_type.enumv() == DTypeEnum::QuantizedS32 ||
-         param.grad_type.enumv() == DTypeEnum::Int32)) {
-        if (sm_algo_pack.i8x8x32_direct_stride1_sdot.usable(this, param)) {
-            ret.insert(ret.begin(), &sm_algo_pack.i8x8x32_direct_stride1_sdot);
-        }
-        if (sm_algo_pack.i8x8x32_direct_stride2_sdot.usable(this, param)) {
-            ret.insert(ret.begin(), &sm_algo_pack.i8x8x32_direct_stride2_sdot);
-        }
-    } else if (param.filter_type.enumv() == DTypeEnum::Quantized8Asymm &&
-               param.grad_type.enumv() == DTypeEnum::QuantizedS32) {
-        if (sm_algo_pack.quint8_direct_stride1_udot.usable(this, param)) {
-            ret.insert(ret.begin(), &sm_algo_pack.quint8_direct_stride1_udot);
-        }
-        if (sm_algo_pack.quint8_direct_stride2_udot.usable(this, param)) {
-            ret.insert(ret.begin(), &sm_algo_pack.quint8_direct_stride2_udot);
-        }
-    }
-#endif
-    return ret;
-}
 const char* ConvolutionBackwardDataImpl::get_algorithm_set_name() const {
     // arm common version 0
     return "DeconvAC0";

dnn/src/arm_common/convolution/opr_impl.h

@@ -47,11 +47,14 @@ protected:
     size_t ncb_1g_get_workspace(Algorithm* algo,
                                 const NCBKernSizeParam& param) override;
 
-    std::vector<Algorithm*> ncb_1g_get_all_algorithms(
-            const NCBKernSizeParam& param) override;
-
     const char* get_algorithm_set_name() const override;
 
+    SmallVector<fallback::ConvolutionBackwardDataImpl::AlgoBase*>
+    get_all_packed_algo() override;
+
+public:
+    MEGDNN_FB_DECL_GET_ALGO_FROM_DESC(ConvolutionBackwardDataImpl);
+
 private:
 #if __ARM_FEATURE_DOTPROD
     class AlgoSdot8DirectStride1;
@@ -59,8 +62,8 @@ private:
     class AlgoUdot8DirectStride1;
     class AlgoUdot8DirectStride2;
 #endif
-    struct AlgoPack;
-    static AlgoPack sm_algo_pack;
+    class AlgoPack;
+    static const AlgoPack& algo_pack();
 };
 
 }  // namespace arm_common

dnn/src/arm_common/convolution/quint8/algos.h

@@ -36,6 +36,7 @@ public:
     ncb_kern_t dispatch_kern(fallback::ConvolutionBackwardDataImpl*,
                              const NCBKernSizeParam&) const override;
 
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_STRD1_DOT_QU8)
 };
 
 class ConvolutionBackwardDataImpl::AlgoUdot8DirectStride2 final
@@ -55,6 +56,7 @@ public:
     ncb_kern_t dispatch_kern(fallback::ConvolutionBackwardDataImpl*,
                              const NCBKernSizeParam&) const override;
 
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_DIRECT_STRD2_DOT_QU8)
 };
 #endif
 }  // namespace arm_common

dnn/src/arm_common/convolution/quint8/conv_backdata_stride1.cpp

@@ -1236,6 +1236,9 @@ bool deconv::can_stride1_quint8_dot(const NCBKernSizeParam& param) {
                     (FH == 2 || FH == 3 || FH == 5 || FH == 7) &&
                     FH >= PH + 1 && FW >= PW + 1;
 
+    avaiable &= (param.filter_type.enumv() == DTypeEnum::Quantized8Asymm ||
+                 param.grad_type.enumv() == DTypeEnum::Int32);
+
     /**
      * \note In the kernel, we use int32_t to calc the value, in order
      * not generate negative number, we first initialize SHIFT and sub

dnn/src/arm_common/convolution/quint8/conv_backdata_stride2.cpp

@@ -1337,6 +1337,9 @@ bool deconv::can_stride2_quint8_dot(const NCBKernSizeParam& param) {
                     (FH == 2 || FH == 3 || FH == 5 || FH == 7) &&
                     FH >= PH + 1 && FW >= PW + 1;
 
+    avaiable &= (param.filter_type.enumv() == DTypeEnum::Quantized8Asymm ||
+                 param.grad_type.enumv() == DTypeEnum::Int32);
+
     /**
      * \note In the kernel, we use uint32_t to calc the value, in order
      * not generate negative number, we first initialize SHIFT and sub

dnn/src/arm_common/elemwise/opr_impl.h

@@ -59,6 +59,7 @@ public:
     virtual bool is_available(const KernParam&) const = 0;
     virtual void exec(const KernParam&) const = 0;
     virtual ~AlgoBase() = default;
+    uint32_t type() const override { return INVALID_ALGO_TYPE; };
 };
 
 #if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC

dnn/src/arm_common/matrix_mul/algos.h

@@ -26,6 +26,7 @@ public:
     kern_t get_kern(const KernSizeParam&) const override;
     PackMode packmode() const override { return PackMode::NO_PACK; }
     MEGDNN_OVERRIDE_MATMUL_DESC(8, 16, 1, 4, AlgoDataType::INT8X8X16, DEFAULT)
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_INT8X8X16)
 };
 
 class MatrixMulImpl::AlgoInt8x8x32Gemv : public AlgoBase {
@@ -39,6 +40,7 @@ public:
     AlgoSet algoset() const override { return AlgoSet::ALGO_TYPE_GEMV; }
     PackMode packmode() const override { return PackMode::NO_PACK; }
     MEGDNN_OVERRIDE_MATMUL_DESC(8, 16, 1, 2, AlgoDataType::QINT8X8X32, DEFAULT)
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_INT8X8X32_GEMV)
 };
 
 class MatrixMulImpl::AlgoInt8x8x32GemvMK4 : public AlgoBase {
@@ -52,6 +54,7 @@ public:
     AlgoSet algoset() const override { return AlgoSet::ALGO_TYPE_GEMV; }
     PackMode packmode() const override { return PackMode::NO_PACK; }
     MEGDNN_OVERRIDE_MATMUL_DESC(8, 16, 1, 2, AlgoDataType::QINT8X8X32, MK4)
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_INT8X8X32_GEMV_MK4)
 };
 
 #if __ARM_FEATURE_DOTPROD
@@ -66,6 +69,7 @@ public:
     AlgoSet algoset() const override { return AlgoSet::ALGO_TYPE_GEMV; }
     PackMode packmode() const override { return PackMode::NO_PACK; }
     MEGDNN_OVERRIDE_MATMUL_DESC(8, 16, 1, 2, AlgoDataType::QINT8X8X32, MK4_DOT)
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_INT8X8X32_GEMV_MK4_DOT)
 };
 #endif
 
@@ -96,6 +100,7 @@ public:
     AlgoSet algoset() const override { return AlgoSet::ALGO_TYPE_GEMV; }
     PackMode packmode() const override { return PackMode::NO_PACK; }
     MEGDNN_OVERRIDE_MATMUL_DESC(4, 1, 1, 4, AlgoDataType::FLOAT32, MK4)
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_F32_GEMV_MK4)
 };
 
 #if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
@@ -110,6 +115,7 @@ public:
     AlgoSet algoset() const override { return AlgoSet::ALGO_TYPE_GEMV; }
     PackMode packmode() const override { return PackMode::NO_PACK; }
     MEGDNN_OVERRIDE_MATMUL_DESC(8, 16, 1, 2, AlgoDataType::FLOAT16, DEFAULT)
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_F16_GEMV)
 };
 #endif
 
@@ -130,6 +136,7 @@ public:
                     static_cast<uint32_t>(AlgoDataType::FLOAT32) |
                     static_cast<uint32_t>(AlgoDataType::QINT8X8X32)),
             DEFAULT)
+    MEGDNN_DECL_ALGO_TYPE(ARM_COMMON_GEVM)
 };
 
 }  // namespace arm_common

dnn/src/arm_common/matrix_mul/opr_impl.cpp

@@ -28,28 +28,47 @@ class MatrixMulImpl::AlgoPack : NonCopyableObj {
     AlgoGevm gevm;
     AlgoF32GemvMK4 f32_gemv_mk4;
 
+    SmallVector<fallback::MatrixMulImpl::AlgoBase*> m_all_algos;
+    fallback::MatrixMulImpl::AlgoBase::Mapper m_all_algos_map;
+
 public:
     AlgoPack() {
-        all_algos.emplace_back(&int8x8x16);
+        m_all_algos.emplace_back(&int8x8x16);
 #if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-        all_algos.emplace_back(&f16gemv);
+        m_all_algos.emplace_back(&f16gemv);
 #endif
 #if __ARM_FEATURE_DOTPROD
-        all_algos.emplace_back(&int8x8x32_gemv_mk4_dot);
+        m_all_algos.emplace_back(&int8x8x32_gemv_mk4_dot);
 #endif
-        all_algos.emplace_back(&int8x8x32_gemv);
-        all_algos.emplace_back(&int8x8x32_gemv_mk4);
-        all_algos.emplace_back(&f32_gemv_mk4);
-        all_algos.emplace_back(&gevm);
+        m_all_algos.emplace_back(&int8x8x32_gemv);
+        m_all_algos.emplace_back(&int8x8x32_gemv_mk4);
+        m_all_algos.emplace_back(&f32_gemv_mk4);
+        m_all_algos.emplace_back(&gevm);
+
+        for (auto&& algo : m_all_algos) {
+            m_all_algos_map.emplace(algo->info().desc, algo);
+        }
+    }
+
+    const SmallVector<fallback::MatrixMulImpl::AlgoBase*>& all_algos() const {
+        return m_all_algos;
     }
-    SmallVector<fallback::MatrixMulImpl::AlgoBase*> all_algos;
+    const AlgoBase::Mapper& all_algos_map() const { return m_all_algos_map; }
 };
 
-SmallVector<fallback::MatrixMulImpl::AlgoBase*> MatrixMulImpl::algo_pack() {
+const MatrixMulImpl::AlgoPack& MatrixMulImpl::algo_pack() {
+    static AlgoPack algo_pack;
+    return algo_pack;
+}
+
+MEGDNN_FB_DEF_GET_ALGO_FROM_DESC(MatrixMulImpl)
+
+SmallVector<fallback::MatrixMulImpl::AlgoBase*>
+MatrixMulImpl::get_all_packed_algo() {
     static AlgoPack s_algo_pack;
-    auto&& algos = fallback::MatrixMulImpl::algo_pack();
-    algos.insert(algos.begin(), s_algo_pack.all_algos.begin(),
-                 s_algo_pack.all_algos.end());
+    auto&& algos = fallback::MatrixMulImpl::get_all_packed_algo();
+    algos.insert(algos.begin(), algo_pack().all_algos().begin(),
+                 algo_pack().all_algos().end());
     return std::move(algos);
 }
 

dnn/src/arm_common/matrix_mul/opr_impl.h

@@ -11,6 +11,7 @@
 #pragma once
 #include "src/common/utils.h"
 #include "src/fallback/matrix_mul/opr_impl.h"
+#include "src/common/algo_base.h"
 
 namespace megdnn {
 namespace arm_common {
@@ -27,7 +28,10 @@ public:
             }
     };
 
-    SmallVector<fallback::MatrixMulImpl::AlgoBase*> algo_pack() override;
+    SmallVector<fallback::MatrixMulImpl::AlgoBase*> get_all_packed_algo()
+            override;
+
+    MEGDNN_FB_DECL_GET_ALGO_FROM_DESC(MatrixMulImpl);
 
 protected:
     class AlgoF32Gemv;               // Arm_common F32 Gemv
@@ -43,6 +47,9 @@ protected:
 #endif
     class AlgoInt8x8x16;  // Arm_common Int 8x8x16
     class AlgoPack;
+
+public:
+    static const AlgoPack& algo_pack();
 };
 
 }  // namespace arm_common

dnn/src/arm_common/pooling/opr_impl.h

@@ -10,6 +10,7 @@
  * implied.
  */
 #pragma once
+#include "megdnn/oprs/base.h"
 #include "src/fallback/pooling/opr_impl.h"
 
 namespace megdnn {
@@ -72,6 +73,8 @@ public:
         virtual ~AlgoBase() = default;
         virtual bool usable(const PoolingKernSizeParam& param) const = 0;
         virtual void exec(const PoolingKernParam& param) const = 0;
+
+        uint32_t type() const override { return INVALID_ALGO_TYPE; };
     };
 
 private:

dnn/src/armv7/conv_bias/int8/algos.h

@@ -40,6 +40,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::QINT8X8X32, AlgoCategory::IM2COL};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARMV7_MATMUL_S8)
 };
 
 }  // namespace armv7

dnn/src/armv7/conv_bias/opr_impl.cpp

@@ -24,22 +24,40 @@ using namespace armv7;
 class ConvBiasImpl::AlgoPack : NonCopyableObj {
     AlgoS8MatrixMul s8_matrix_mul;
     AlgoQU8MatrixMul qu8_matrix_mul;
+    fallback::ConvBiasImpl::AlgoBase::Mapper m_all_algos_map;
+    SmallVector<fallback::ConvBiasImpl::AlgoBase*> m_all_algos;
 public:
     AlgoPack() {
-        all_algos.emplace_back(&qu8_matrix_mul);
-        all_algos.emplace_back(&s8_matrix_mul);
+        m_all_algos.emplace_back(&qu8_matrix_mul);
+        m_all_algos.emplace_back(&s8_matrix_mul);
+
+        for (auto&& algo : m_all_algos) {
+            m_all_algos_map.emplace(algo->info().desc, algo);
+        }
+    }
+
+    const SmallVector<fallback::ConvBiasImpl::AlgoBase*>& all_algos()
+            const {
+        return m_all_algos;
     }
-    SmallVector<AlgoBase*> all_algos;
+    const AlgoBase::Mapper& all_algos_map() const { return m_all_algos_map; }
 };
 
-SmallVector<fallback::ConvBiasImpl::AlgoBase*> ConvBiasImpl::algo_pack() {
-    static AlgoPack sl_algo_pack;
-    auto&& algos = arm_common::ConvBiasImpl::algo_pack();
+const ConvBiasImpl::AlgoPack& ConvBiasImpl::algo_pack() {
+    static AlgoPack algo_pack;
+    return algo_pack;
+}
+
+MEGDNN_FB_DEF_GET_ALGO_FROM_DESC(ConvBiasImpl)
+
+SmallVector<fallback::ConvBiasImpl::AlgoBase*>
+ConvBiasImpl::get_all_packed_algo() {
+    auto&& algos = arm_common::ConvBiasImpl::get_all_packed_algo();
     //! TODO fused matmul bias is slower than matmul + elemwise in armv7 now,
     //! and nearly equal in aarch64, because of the waste of register in
     //! postprocess
-    algos.insert(algos.end(), sl_algo_pack.all_algos.begin(),
-                 sl_algo_pack.all_algos.end());
+    algos.insert(algos.end(), algo_pack().all_algos().begin(),
+                 algo_pack().all_algos().end());
     return std::move(algos);
 }
 

dnn/src/armv7/conv_bias/opr_impl.h

@@ -25,7 +25,9 @@ public:
         }
     };
 
-    SmallVector<fallback::ConvBiasImpl::AlgoBase*> algo_pack() override;
+    SmallVector<fallback::ConvBiasImpl::AlgoBase*> get_all_packed_algo() override;
+
+    MEGDNN_FB_DECL_GET_ALGO_FROM_DESC(ConvBiasImpl);
 
 protected:
     const char* get_algorithm_set_name() const override;
@@ -34,6 +36,7 @@ private:
     class AlgoS8MatrixMul;
     class AlgoQU8MatrixMul;
     class AlgoPack;
+    static const AlgoPack& algo_pack();
 };
 
 }  // namespace armv7

dnn/src/armv7/conv_bias/quint8/algos.h

@@ -42,6 +42,7 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::QUINT8X8X32, AlgoCategory::IM2COL};
     }
+    MEGDNN_DECL_ALGO_TYPE(ARMV7_MATMUL_QU8)
 };
 
 }  // namespace armv7

dnn/src/armv7/matrix_mul/algos.h

@@ -27,6 +27,7 @@ public:
     size_t get_workspace(const KernSizeParam&) const override;
     kern_t get_kern(const KernSizeParam&) const override;
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(ARMV7_F32)
 };
 
 class MatrixMulImpl::AlgoF32MK4Pack4x12 final : public AlgoBase {
@@ -37,6 +38,7 @@ public:
     size_t get_workspace(const KernSizeParam&) const override;
     kern_t get_kern(const KernSizeParam&) const override;
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(ARMV7_F32_MK4_PACK_4X12)
 };
 
 class MatrixMulImpl::AlgoF32MK4_4x8 final : public AlgoBase {
@@ -48,6 +50,7 @@ public:
     kern_t get_kern(const KernSizeParam&) const override;
     PackMode packmode() const override { return PackMode::NO_PACK; }
     MEGDNN_OVERRIDE_MATMUL_DESC(4, 8, 4, 4, AlgoDataType::FLOAT32, MK4)
+    MEGDNN_DECL_ALGO_TYPE(ARMV7_F32_MK4_4x8)
 };
 
 #if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
@@ -59,6 +62,7 @@ public:
     size_t get_workspace(const KernSizeParam&) const override;
     kern_t get_kern(const KernSizeParam&) const override;
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(ARMV7_F16_K4X16X1)
 };
 class MatrixMulImpl::AlgoF16MK8_4x8 final : public AlgoBase {
 public:
@@ -69,6 +73,7 @@ public:
     kern_t get_kern(const KernSizeParam&) const override;
     PackMode packmode() const override { return PackMode::NO_PACK; }
     MEGDNN_OVERRIDE_MATMUL_DESC(4, 8, 8, 2, AlgoDataType::FLOAT16, MK8)
+    MEGDNN_DECL_ALGO_TYPE(ARMV7_F16_MK8_4X8)
 };
 #endif
 #if __ARM_FEATURE_DOTPROD
@@ -80,6 +85,7 @@ public:
     size_t get_workspace(const KernSizeParam&) const override;
     kern_t get_kern(const KernSizeParam&) const override;
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(ARMV7_INT8_K6X8X4)
 };
 
 class MatrixMulImpl::AlgoQuint8DotK4x8x4 final : public AlgoBase {
@@ -90,6 +96,7 @@ public:
     size_t get_workspace(const KernSizeParam&) const override;
     kern_t get_kern(const KernSizeParam&) const override;
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(ARMV7_QUINT8_K4X8X4)
 };
 
 class MatrixMulImpl::AlgoInt8x8x32MK4_8x4x4DotProd final : public AlgoBase {
@@ -102,11 +109,18 @@ public:
     size_t get_workspace(const KernSizeParam&) const override;
     kern_t get_kern(const KernSizeParam&) const override;
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(ARMV7_INT8_MK4_8X4X4_DOTPROD)
 };
 #endif
 
 class MatrixMulImpl::AlgoF32Gemv final
-        : public arm_common::MatrixMulImpl::AlgoF32Gemv {};
+        : public arm_common::MatrixMulImpl::AlgoF32Gemv {
+public:
+    AlgoF32Gemv() : arm_common::MatrixMulImpl::AlgoF32Gemv() {
+        m_handle_type = Handle::HandleType::ARMV7;
+    }
+    MEGDNN_DECL_ALGO_TYPE(ARMV7_F32_GEMV)
+};
 
 class MatrixMulImpl::AlgoInt8x8x32K4x2x16 final : public AlgoBase {
 public:
@@ -117,6 +131,7 @@ public:
     size_t get_workspace(const KernSizeParam&) const override;
     kern_t get_kern(const KernSizeParam&) const override;
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(ARMV7_INT8X8X32_K4X2X16)
 };
 
 class MatrixMulImpl::AlgoInt8x8x32K4x8x8 final : public AlgoBase {
@@ -128,6 +143,7 @@ public:
     size_t get_workspace(const KernSizeParam&) const override;
     kern_t get_kern(const KernSizeParam&) const override;
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(ARMV7_INT8X8X32_K4X8X8)
 };
 
 class MatrixMulImpl::AlgoQuint8K4x8x8 final : public AlgoBase {
@@ -138,6 +154,7 @@ public:
     size_t get_workspace(const KernSizeParam&) const override;
     kern_t get_kern(const KernSizeParam&) const override;
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(ARMV7_QUINT8_K4X8X8)
 };
 
 class MatrixMulImpl::AlgoInt8x8x16K4x2x16 final : public AlgoBase {
@@ -149,6 +166,7 @@ public:
     size_t get_workspace(const KernSizeParam&) const override;
     kern_t get_kern(const KernSizeParam&) const override;
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(ARMV7_INT8X8X16_K4X2X16)
 };
 
 class MatrixMulImpl::AlgoInt8x8x16K4x8x8 final : public AlgoBase {
@@ -160,6 +178,7 @@ public:
     size_t get_workspace(const KernSizeParam&) const override;
     kern_t get_kern(const KernSizeParam&) const override;
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(ARMV7_INT8X8X16_K4X8X8)
 };
 
 class MatrixMulImpl::AlgoInt8x8x16MK4_8x8x4 final : public AlgoBase {
@@ -171,6 +190,7 @@ public:
     size_t get_workspace(const KernSizeParam&) const override;
     kern_t get_kern(const KernSizeParam&) const override;
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(ARMV7_INT8X8X16_MK4_K8X8X4)
 };
 
 class MatrixMulImpl::AlgoInt16x16x32K12x4x1 final : public AlgoBase {
@@ -182,6 +202,7 @@ public:
     size_t get_workspace(const KernSizeParam&) const override;
     kern_t get_kern(const KernSizeParam&) const override;
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(ARMV7_INT16X16X32_K12X4X1)
 };
 
 class MatrixMulImpl::AlgoInt16x16x32MK8_4x8 final : public AlgoBase {
@@ -193,6 +214,7 @@ public:
     kern_t get_kern(const KernSizeParam&) const override;
     PackMode packmode() const override { return PackMode::NO_PACK; }
     MEGDNN_OVERRIDE_MATMUL_DESC(4, 8, 8, 2, AlgoDataType::INT16X16X32, MK8)
+    MEGDNN_DECL_ALGO_TYPE(ARMV7_INT16X16X32_MK8_4X8)
 };
 
 class MatrixMulImpl::AlgoInt8x8x32MK4_4x2x16 final : public AlgoBase {
@@ -204,6 +226,7 @@ public:
     size_t get_workspace(const KernSizeParam&) const override;
     kern_t get_kern(const KernSizeParam&) const override;
     MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
+    MEGDNN_DECL_ALGO_TYPE(ARMV7_INT8X8X32_MK4_4X2X16)
 };
 
 }  // namespace armv7

dnn/src/armv7/matrix_mul/opr_impl.cpp

@@ -43,42 +43,60 @@ class MatrixMulImpl::AlgoPack : NonCopyableObj {
     AlgoInt16x16x32K12x4x1 int16x16x32_k12x4x1;
     AlgoInt16x16x32MK8_4x8 int16x16x32_mk8_4x8;
 
+    SmallVector<fallback::MatrixMulImpl::AlgoBase*> m_all_algos;
+    fallback::MatrixMulImpl::AlgoBase::Mapper m_all_algos_map;
+
 public:
-    SmallVector<fallback::MatrixMulImpl::AlgoBase*> all_algos;
 
     AlgoPack() {
-        all_algos.emplace_back(&f32_gemv);
-        all_algos.emplace_back(&f32);
-        all_algos.emplace_back(&f32_mk4_pack_4x12);
-        all_algos.emplace_back(&f32_mk4_4x8);
+        m_all_algos.emplace_back(&f32_gemv);
+        m_all_algos.emplace_back(&f32);
+        m_all_algos.emplace_back(&f32_mk4_pack_4x12);
+        m_all_algos.emplace_back(&f32_mk4_4x8);
 #if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-        all_algos.emplace_back(&f16_k4x16x1);
-        all_algos.emplace_back(&f16_mk8_4x8);
+        m_all_algos.emplace_back(&f16_k4x16x1);
+        m_all_algos.emplace_back(&f16_mk8_4x8);
 #endif
 #if __ARM_FEATURE_DOTPROD
-        all_algos.emplace_back(&int8x8x32_mk4_8x4x4_dotprod);
-        all_algos.emplace_back(&int8_k6x8x4);
-        all_algos.emplace_back(&quint8_k4x8x4);
+        m_all_algos.emplace_back(&int8x8x32_mk4_8x4x4_dotprod);
+        m_all_algos.emplace_back(&int8_k6x8x4);
+        m_all_algos.emplace_back(&quint8_k4x8x4);
 #endif
-        all_algos.emplace_back(&int8x8x32_mk4_4x2x16);
-        all_algos.emplace_back(&int8x8x32_k4x2x16);
-        all_algos.emplace_back(&int8x8x32_k4x8x8);
-        all_algos.emplace_back(&quint8_k4x8x8);
-        all_algos.emplace_back(&int8x8x16_mk4_8x8x4);
-        all_algos.emplace_back(&int8x8x16_k4x2x16);
-        all_algos.emplace_back(&int8x8x16_k4x8x8);
+        m_all_algos.emplace_back(&int8x8x32_mk4_4x2x16);
+        m_all_algos.emplace_back(&int8x8x32_k4x2x16);
+        m_all_algos.emplace_back(&int8x8x32_k4x8x8);
+        m_all_algos.emplace_back(&quint8_k4x8x8);
+        m_all_algos.emplace_back(&int8x8x16_mk4_8x8x4);
+        m_all_algos.emplace_back(&int8x8x16_k4x2x16);
+        m_all_algos.emplace_back(&int8x8x16_k4x8x8);
+
+        m_all_algos.emplace_back(&int16x16x32_k12x4x1);
+        m_all_algos.emplace_back(&int16x16x32_mk8_4x8);
+
+        for (auto&& algo : m_all_algos) {
+            m_all_algos_map.emplace(algo->info().desc, algo);
+        }
+    }
 
-        all_algos.emplace_back(&int16x16x32_k12x4x1);
-        all_algos.emplace_back(&int16x16x32_mk8_4x8);
+    const SmallVector<fallback::MatrixMulImpl::AlgoBase*>& all_algos() const {
+        return m_all_algos;
     }
+    const AlgoBase::Mapper& all_algos_map() const { return m_all_algos_map; }
 };
 
-SmallVector<fallback::MatrixMulImpl::AlgoBase*> MatrixMulImpl::algo_pack() {
-    static AlgoPack s_algo_pack;
-    auto algos = arm_common::MatrixMulImpl::algo_pack();
-    algos.insert(algos.begin(), s_algo_pack.all_algos.begin(),
-                 s_algo_pack.all_algos.end());
+const MatrixMulImpl::AlgoPack& MatrixMulImpl::algo_pack() {
+    static AlgoPack algo_pack;
+    return algo_pack;
+}
+
+SmallVector<fallback::MatrixMulImpl::AlgoBase*>
+MatrixMulImpl::get_all_packed_algo() {
+    auto algos = arm_common::MatrixMulImpl::get_all_packed_algo();
+    algos.insert(algos.begin(), algo_pack().all_algos().begin(),
+                 algo_pack().all_algos().end());
     return algos;
 }
 
+MEGDNN_FB_DEF_GET_ALGO_FROM_DESC(MatrixMulImpl)
+
 // vim: syntax=cpp.doxygen

dnn/src/armv7/matrix_mul/opr_impl.h

@@ -25,7 +25,10 @@ public:
         }
     };
 
-    SmallVector<fallback::MatrixMulImpl::AlgoBase*> algo_pack() override;
+    SmallVector<fallback::MatrixMulImpl::AlgoBase*> get_all_packed_algo()
+            override;
+
+    MEGDNN_FB_DECL_GET_ALGO_FROM_DESC(MatrixMulImpl);
 
 private:
     class AlgoF32;                  // Armv7 F32
@@ -52,6 +55,9 @@ private:
                                           // DotProduct
 #endif
     class AlgoPack;
+
+public:
+    static const AlgoPack& algo_pack();
 };
 
 }  // namespace armv7

dnn/src/common/algo_base.h

@@ -0,0 +1,101 @@
+/**
+ * \file dnn/src/common/algo_base.h
+ * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+ *
+ * Copyright (c) 2014-2020 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 <functional>
+#include <string>
+
+#include "megdnn/oprs/base.h"
+#include "src/common/utils.h"
+
+namespace megdnn {
+
+#define MEGDNN_DECL_ALGO_TYPE(_type)                              \
+    uint32_t type() const override {                              \
+        return static_cast<std::underlying_type<AlgoType>::type>( \
+                AlgoType::_type);                                 \
+    }
+
+#define MEGDNN_FB_DECL_GET_ALGO_FROM_DESC(_opr)          \
+    static fallback::_opr::AlgoBase* get_algo_from_desc( \
+            const AlgorithmDesc& desc)
+
+#define MEGDNN_FB_DEF_GET_ALGO_FROM_DESC(_opr)                  \
+    fallback::_opr::AlgoBase* _opr::get_algo_from_desc(         \
+            const AlgorithmDesc& desc) {                        \
+        megdnn_assert(algo_pack().all_algos_map().find(desc) != \
+                      algo_pack().all_algos_map().end());       \
+        return algo_pack().all_algos_map().at(desc);            \
+    }
+
+#define MEGDNN_DEF_GET_ALGO_FROM_DESC(_opr)                               \
+    _opr::AlgoBase* _opr::get_algo_from_desc(const AlgorithmDesc& desc) { \
+        megdnn_assert(algo_pack().all_algos_map().find(desc) !=           \
+                      algo_pack().all_algos_map().end());                 \
+        return algo_pack().all_algos_map().at(desc);                      \
+    }
+
+/**
+ * \brief construct algo from AlgorithmDesc
+ */
+template <typename AlgoBase>
+class AlgoConstructMixin {
+private:
+    std::vector<std::unique_ptr<AlgoBase>> m_refhold;
+protected:
+    typename AlgoBase::Mapper m_all_algos_map;
+
+public:
+
+    //! construct the algo which described by desc, and return the instance
+    AlgoBase* construct_and_get_algo(
+            const detail::Algorithm::Info::Desc& desc) {
+        auto iter = m_all_algos_map.find(desc);
+        if (iter != m_all_algos_map.end()) {
+            return m_all_algos_map.at(desc);
+        }
+        std::string serialized_bin;
+        AlgoBase::serialize_write_pod(desc.type, serialized_bin);
+        serialized_bin += desc.param;
+        m_refhold.emplace_back(AlgoBase::deserialize(serialized_bin));
+        m_all_algos_map.emplace(desc, m_refhold.back().get());
+        return m_refhold.back().get();
+    }
+
+    void clear() {
+        m_all_algos_map.clear();
+        m_refhold.clear();
+    }
+
+    const typename AlgoBase::Mapper& all_algos_map() const {
+        return m_all_algos_map;
+    }
+};
+
+}  // namespace megdnn
+
+namespace std {
+template <>
+struct hash<megdnn::detail::Algorithm::Info::Desc> {
+    std::size_t operator()(
+            const megdnn::detail::Algorithm::Info::Desc& desc) const {
+        return megdnn::hash_combine<size_t>(
+                megdnn::hash_combine<size_t>(
+                        std::hash<std::string>()(desc.param),
+                        std::hash<uint32_t>()(desc.type)),
+                std::hash<uint32_t>()(static_cast<uint32_t>(desc.handle_type)));
+    }
+};
+}  // namespace std
+
+// vim: syntax=cpp.doxygen

dnn/src/common/algo_chooser.h

@@ -25,15 +25,34 @@ namespace megdnn {
  */
 template <class Opr, typename... Args>
 typename Opr::AlgoBase* get_algorithm(Opr* opr, Args&&... args) {
-    typename Opr::Algorithm* ret;
-    if (auto set = opr->execution_policy().algorithm) {
+    typename Opr::AlgorithmInfo ret;
+    auto set = opr->execution_policy().algo;
+    if (set.valid()) {
         ret = set;
     } else {
-        ret = opr->get_algorithm_heuristic(std::forward<Args>(args)...,
-                                           std::numeric_limits<size_t>::max(),
-                                           false);
+        ret = opr->get_algorithm_info_heuristic(
+                std::forward<Args>(args)..., std::numeric_limits<size_t>::max(),
+                false);
+    }
+    return opr->get_algo_from_desc(ret.desc);
+}
+
+/*!
+ * \brief get user-configured algorithm, or heuristic algorithm. used in opencl
+ * whose algo need to be constructed each time.
+ */
+template <class Opr, typename... Args>
+typename Opr::AlgoBase* get_algorithm_or_construct(Opr* opr, Args&&... args) {
+    typename Opr::AlgorithmInfo ret;
+    auto set = opr->execution_policy().algo;
+    if (set.valid()) {
+        return opr->algo_pack().construct_and_get_algo(set.desc);
+    } else {
+        ret = opr->get_algorithm_info_heuristic(
+                std::forward<Args>(args)..., std::numeric_limits<size_t>::max(),
+                false);
+        return opr->get_algo_from_desc(ret.desc);
     }
-    return static_cast<typename Opr::AlgoBase*>(ret);
 }
 
 /*!

dnn/src/common/utils.h

@@ -9,6 +9,32 @@
  * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  */
 
+/**
+ * Boost Software License - Version 1.0 - August 17th, 2003
+ *
+ * Permission is hereby granted, free of charge, to any person or organization
+ * obtaining a copy of the software and accompanying documentation covered by
+ * this license (the "Software") to use, reproduce, display, distribute,
+ * execute, and transmit the Software, and to prepare derivative works of the
+ * Software, and to permit third-parties to whom the Software is furnished to
+ * do so, all subject to the following:
+ *
+ * The copyright notices in the Software and this entire statement, including
+ * the above license grant, this restriction and the following disclaimer,
+ * must be included in all copies of the Software, in whole or in part, and
+ * all derivative works of the Software, unless such copies or derivative
+ * works are solely in the form of machine-executable object code generated by
+ * a source language processor.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT
+ * SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE
+ * FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE,
+ * ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+ * DEALINGS IN THE SOFTWARE.
+ */
+
 #pragma once
 
 #include "megdnn/arch.h"
@@ -263,6 +289,13 @@ constexpr uint32_t operator"" _hash(char const* str, size_t count) {
     return XXHash64CT::hash(str, count, 20160701);
 }
 
+// refer to https://www.boost.org/doc/libs/1_64_0/boost/functional/hash/hash.hpp
+template <typename T>
+inline T hash_combine(T seed, T value) {
+    seed ^= value + 0x9e3779b9 + (seed << 6) + (seed >> 2);
+    return seed;
+}
+
 template <typename Vec>
 std::string vec2str(Vec&& vec) {
     std::string res;

dnn/src/cuda/batch_conv_bias/algo.cpp

@@ -18,8 +18,14 @@ using namespace cuda;
 BatchConvBiasForwardImpl::AlgoPack::AlgoPack() {
     all_algos.push_back(&int8_nchw4_gemm_dotprod);
     all_algos.push_back(&int8_nchw4_implicit_gemm_dotprod);
+
+    for (auto&& algo : all_algos) {
+        m_all_algos_map.emplace(algo->info().desc, algo);
+    }
 }
 
+MEGDNN_DEF_GET_ALGO_FROM_DESC(BatchConvBiasForwardImpl)
+
 BatchConvBiasForwardImpl::AlgoPack BatchConvBiasForwardImpl::sm_algo_pack;
 
 BatchConvBiasForwardImpl::AlgoBase::SizeArgs::SizeArgs(

dnn/src/cuda/batch_conv_bias/algo.h

@@ -11,13 +11,16 @@
 
 #pragma once
 
-#include <csetjmp>
+#include <unordered_map>
 #include "megdnn/oprs.h"
 
 #include "src/common/utils.h"
 #include "src/cuda/batch_conv_bias/opr_impl.h"
 #include "src/cuda/handle.h"
 
+#include "src/common/algo_base.h"
+#include "src/common/metahelper.h"
+
 namespace megdnn {
 namespace cuda {
 
@@ -26,6 +29,12 @@ protected:
     ~AlgoBase() = default;
 
 public:
+    enum class AlgoType : uint32_t {
+        CUDA_GEMM_NCHW4_DOTPROD_INT8,
+        CUDA_IMPLICIT_GEMM_PRECOMP_NCHW4_DOTPROD_INT8,
+    };
+    using Mapper = std::unordered_map<AlgorithmDesc, AlgoBase*>;
+
     AlgoBase() : Algorithm() { m_handle_type = Handle::HandleType::CUDA; }
     struct SizeArgs {
         BatchConvBiasForwardImpl* opr;
@@ -85,6 +94,7 @@ public:
     const char* name() const override {
         return "BATCH_CONV_BIAS_INT8_NCHW4_GEMM_DOTPROD";
     }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_GEMM_NCHW4_DOTPROD_INT8)
 };
 
 class BatchConvBiasForwardImpl::AlgoInt8NCHW4DotProdImplicitGemmPrecomp final
@@ -99,15 +109,16 @@ public:
     const char* name() const override {
         return "BATCH_CONV_BIAS_INT8_NCHW4_IMPLICIT_GEMM_PRECOMP_DOTPROD";
     }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_IMPLICIT_GEMM_PRECOMP_NCHW4_DOTPROD_INT8)
 
 private:
     WorkspaceBundle get_workspace_bundle(dt_byte* raw_ptr,
                                          const SizeArgs& args) const;
 };
 
-class BatchConvBiasForwardImpl::AlgoPack {
-    AlgoPack(const AlgoPack&) = delete;
-    AlgoPack& operator=(const AlgoPack&) = delete;
+class BatchConvBiasForwardImpl::AlgoPack : NonCopyableObj {
+private:
+    AlgoBase::Mapper m_all_algos_map;
 
 public:
     AlgoPack();
@@ -116,6 +127,8 @@ public:
     AlgoInt8NCHW4DotProdImplicitGemmPrecomp int8_nchw4_implicit_gemm_dotprod;
 
     std::vector<AlgoBase*> all_algos;
+
+    const AlgoBase::Mapper& all_algos_map() const { return m_all_algos_map; }
 };
 
 }  // namespace cuda

dnn/src/cuda/batch_conv_bias/opr_impl.h

@@ -26,6 +26,18 @@ public:
                                   const TensorLayout& bias,
                                   const TensorLayout& z,
                                   const TensorLayout& dst) override;
+    const char* get_algorithm_set_name() const override;
+
+    class AlgoBase;
+    class AlgoInt8NCHW4DotProdGemm;
+    class AlgoInt8NCHW4DotProdImplicitGemmPrecomp;
+
+    class AlgoPack;
+
+    static const AlgoPack& algo_pack() { return sm_algo_pack; }
+    static AlgoBase* get_algo_from_desc(const AlgorithmDesc& desc);
+
+protected:
     std::vector<Algorithm*> get_all_algorithms(
             const TensorLayout& src, const TensorLayout& filter,
             const TensorLayout& bias, const TensorLayout& z,
@@ -37,15 +49,6 @@ public:
                                        const TensorLayout& dst,
                                        size_t workspace_limit_in_bytes,
                                        bool reproducible) override;
-    const char* get_algorithm_set_name() const override;
-
-    class AlgoBase;
-    class AlgoInt8NCHW4DotProdGemm;
-    class AlgoInt8NCHW4DotProdImplicitGemmPrecomp;
-
-    class AlgoPack;
-
-    static const AlgoPack& algo_pack() { return sm_algo_pack; }
 
 private:
     static AlgoPack sm_algo_pack;

dnn/src/cuda/batched_matrix_mul/algo.cpp

@@ -60,4 +60,12 @@ BatchedMatrixMulForwardImpl::AlgoPack::AlgoPack() {
     for (auto& algo : brute_force_algos) {
         all_algos.push_back(&algo);
     }
+
+    for (auto&& algo : all_algos) {
+        m_all_algos_map.emplace(algo->info().desc, algo);
+    }
 }
+
+MEGDNN_DEF_GET_ALGO_FROM_DESC(BatchedMatrixMulForwardImpl)
+
+// vim: syntax=cpp.doxygen

dnn/src/cuda/batched_matrix_mul/algo.h

@@ -16,6 +16,8 @@
 #include "src/common/utils.h"
 #include "src/cuda/batched_matrix_mul/opr_impl.h"
 #include "src/cuda/matrix_mul/cublasLt_wrapper.h"
+#include "src/common/metahelper.h"
+
 #if CUDA_VERSION >= 10010
 #include <cublasLt.h>
 #endif
@@ -28,6 +30,14 @@ protected:
     ~AlgoBase() = default;
 
 public:
+    enum class AlgoType : uint32_t {
+        CUDA_BRUTE_FORCE,
+        CUDA_CUBLAS,
+        CUDA_CUBLASLT,
+        CUDA_INT8X8X32,
+    };
+    using Mapper = std::unordered_map<AlgorithmDesc, AlgoBase*>;
+
     AlgoBase() : Algorithm() { m_handle_type = Handle::HandleType::CUDA; }
     struct SizeArgs {
         BatchedMatrixMulForwardImpl* opr;
@@ -90,6 +100,13 @@ public:
     void exec(const ExecArgs& args) const final;
     bool is_reproducible() const override { return true; }
     const char* name() const override { return m_name.c_str(); }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_BRUTE_FORCE)
+
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_algorithm, ret);
+        return ret;
+    }
 };
 class BatchedMatrixMulForwardImpl::AlgoCublas final
         : public BatchedMatrixMulForwardImpl::AlgoBase {
@@ -100,6 +117,7 @@ public:
     void exec(const ExecArgs& args) const final;
     bool is_reproducible() const override { return true; }
     const char* name() const override { return "CUBLAS"; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_CUBLAS)
 };
 #if CUDA_VERSION >= 10010
 class BatchedMatrixMulForwardImpl::AlgoCublasLt final : public AlgoBase {
@@ -110,6 +128,7 @@ public:
     void exec(const ExecArgs& args) const final;
     bool is_reproducible() const override { return true; }
     const char* name() const override { return "CUBLAS_LT"; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_CUBLASLT)
 };
 #endif
 class BatchedMatrixMulForwardImpl::AlgoInt8x8x32 final
@@ -121,11 +140,13 @@ public:
     void exec(const ExecArgs& args) const final;
     bool is_reproducible() const override { return true; }
     const char* name() const override { return "INT8x8x32"; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_INT8X8X32)
 };
-class BatchedMatrixMulForwardImpl::AlgoPack {
+
+class BatchedMatrixMulForwardImpl::AlgoPack : NonCopyableObj {
+private:
+    AlgoBase::Mapper m_all_algos_map;
     MatrixMulForwardImpl::AlgoPack mm_pack;
-    AlgoPack(const AlgoPack&) = delete;
-    AlgoPack& operator=(const AlgoPack&) = delete;
 
 public:
     AlgoPack();
@@ -137,6 +158,8 @@ public:
     AlgoInt8x8x32 int8x8x32;
     std::vector<AlgoBase*> all_algos;
     std::vector<AlgoBruteForce> brute_force_algos;
+
+    const AlgoBase::Mapper& all_algos_map() const { return m_all_algos_map; }
 };
 }  // namespace cuda
 }  // namespace megdnn

dnn/src/cuda/batched_matrix_mul/brute_force.cpp

@@ -24,7 +24,7 @@ bool BatchedMatrixMulForwardImpl::AlgoBruteForce::is_available(
         const SizeArgs& args) const {
     MatrixMulForwardImpl mm{args.opr->handle()};
     mm.param() = {args.opr->param().transposeA, args.opr->param().transposeB};
-    mm.execution_policy() = {m_algorithm};
+    mm.execution_policy() = {m_algorithm->info()};
 
     auto mm_layout_a = args.layout_a.remove_axis(0);
     auto mm_layout_b = args.layout_b.remove_axis(0);
@@ -39,7 +39,7 @@ size_t BatchedMatrixMulForwardImpl::AlgoBruteForce::get_workspace_in_bytes(
     auto mm_opr = args.opr->handle()->create_operator<MatrixMulForward>();
     mm_opr->param() = {args.opr->param().transposeA,
                        args.opr->param().transposeB};
-    mm_opr->execution_policy() = {m_algorithm};
+    mm_opr->execution_policy() = {m_algorithm->info()};
 
     return mm_opr->get_workspace_in_bytes(args.layout_a, args.layout_b,
                                           args.layout_c);
@@ -50,7 +50,7 @@ void BatchedMatrixMulForwardImpl::AlgoBruteForce::exec(
     auto&& mm_opr = args.opr->handle()->create_operator<MatrixMulForward>();
     mm_opr->param() = {args.opr->param().transposeA,
                        args.opr->param().transposeB};
-    mm_opr->execution_policy() = {m_algorithm};
+    mm_opr->execution_policy() = {m_algorithm->info()};
     rep(n, N) {
         TensorND A_, B_, C_;
         auto tensor_n_from_batch = [n](const TensorND& in, TensorND& out) {

dnn/src/cuda/batched_matrix_mul/opr_impl.h

@@ -32,6 +32,16 @@ public:
               _megdnn_workspace workspace) override;
     size_t get_workspace_in_bytes(const TensorLayout& A, const TensorLayout& B,
                                   const TensorLayout& C) override;
+
+    const char* get_algorithm_set_name() const override {
+        return "BATCHED_MATMUL";
+    }
+
+    bool is_thread_safe() const override { return true; }
+    static const AlgoPack& algo_pack() { return sm_algo_pack; }
+    static AlgoBase* get_algo_from_desc(const AlgorithmDesc& desc);
+
+protected:
     std::vector<Algorithm*> get_all_algorithms(const TensorLayout& A,
                                                const TensorLayout& B,
                                                const TensorLayout& C) override;
@@ -40,12 +50,6 @@ public:
                                        const TensorLayout& C,
                                        size_t workspace_limit_in_bytes,
                                        bool reproducible) override;
-    const char* get_algorithm_set_name() const override {
-        return "BATCHED_MATMUL";
-    }
-
-    bool is_thread_safe() const override { return true; }
-    static const AlgoPack& algo_pack() { return sm_algo_pack; }
 
 private:
     static AlgoPack sm_algo_pack;

dnn/src/cuda/conv_bias/algo.cpp

@@ -100,10 +100,16 @@ ConvBiasForwardImpl::AlgoPack::AlgoPack() {
     for (size_t i = all_algo_size; i < all_algos.size(); ++i) {
         non_cudnn_algos.push_back(all_algos[i]);
     }
+
+    for (auto&& algo : all_algos) {
+        m_all_algos_map.emplace(algo->info().desc, algo);
+    }
 }
 
 ConvBiasForwardImpl::AlgoPack ConvBiasForwardImpl::sm_algo_pack;
 
+MEGDNN_DEF_GET_ALGO_FROM_DESC(ConvBiasForwardImpl)
+
 ConvBiasForwardImpl::AlgoBase::SizeArgs::SizeArgs(
         ConvBiasForwardImpl* o, const TensorLayout& src,
         const TensorLayout& filter, const TensorLayout& bias,
@@ -172,43 +178,10 @@ std::string ConvBiasForwardImpl::AlgoBase::SizeArgs::to_string() const {
 }
 
 void ConvBiasForwardImpl::AlgoPack::fill_cudnn_algos() {
-#define V1(v) #v
-#define V(v) V1(v)
-
-#define DEF_ALGO(NAME, REPROD)                                              \
-    cudnn_conv_bias_activations.push_back(                                  \
-            {REPROD,                                                        \
-             "CUDNN:ConvBiasActivation:" #NAME                              \
-             "v" V(CUDNN_MAJOR) "." V(CUDNN_MINOR) "." V(CUDNN_PATCHLEVEL), \
-             NAME});                                                        \
-    cudnn_convs.push_back(                                                  \
-            {REPROD,                                                        \
-             "CUDNN:Convolution:" #NAME                                     \
-             "v" V(CUDNN_MAJOR) "." V(CUDNN_MINOR) "." V(CUDNN_PATCHLEVEL), \
-             NAME})
-
-    DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM, true);
-    DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM, true);
-    DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_GEMM, true);
-    DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_DIRECT, true);
-    DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_FFT, true);
-    DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING, true);
-
-#if CUDNN_MAJOR >= 5
-    DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD, true);
-#if CUDNN_MAJOR >= 6 || CUDNN_MINOR >= 1
-    DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED, true);
-#endif
-#endif
-
-#if !(CUDNN_MAJOR >= 6 || CUDNN_MINOR >= 1)
-#pragma message "not latest cudnn"
-#endif
-
-#undef DEF_ALGO
-
-#undef V
-#undef V1
+    for (auto&& algo : CudnnAlgoPack::conv_fwd_algos()) {
+        cudnn_conv_bias_activations.push_back(algo.first);
+        cudnn_convs.push_back(algo.first);
+    }
 }
 
 #if CUDA_VERSION >= 10000

dnn/src/cuda/conv_bias/algo.h

@@ -6,19 +6,23 @@
  *
  * 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.
  */
 
 #pragma once
 
 #include "megdnn/oprs.h"
 
+#include "src/common/algo_base.h"
 #include "src/common/utils.h"
+#include "src/common/metahelper.h"
 #include "src/cuda/conv_bias/conv_bias_int8.cuh"
 #include "src/cuda/conv_bias/helper.h"
 #include "src/cuda/conv_bias/opr_impl.h"
 #include "src/cuda/convolution_helper/parameter.cuh"
 #include "src/cuda/handle.h"
+#include "src/cuda/cudnn_wrapper.h"
 
 #include <cuda.h>
 #include <memory>
@@ -38,11 +42,39 @@ protected:
     ~AlgoBase() = default;
 
 public:
+    enum class AlgoType : uint32_t {
+        CUDA_CUDNN_CONVBIAS,
+        CUDA_CHANWISE,
+        CUDA_CHANWISE_SMALL,
+        CUDA_CHANWISE_INT8X8X32,
+        CUDA_CUDNN_CONV,
+        CUDA_INPLACE_MATMUL,
+        CUDA_MATMUL,
+        CUDA_MATMUL_INT8X8X32,
+        CUDA_1X1,
+        CUDA_BATCHED_MATMUL,
+        CUDA_GROUP_CONV_GENERAL,
+        CUDA_WMMA_UINT4X4X32,
+        CUDA_IMPLICIT_GEMM_CHWN4_DOTPROD_INT8,
+        CUDA_IMPLICIT_GEMM_NCHW4_DOTPROD_INT8,
+        CUDA_IMPLICIT_GEMM_CHWN4_IMMA_INT8,
+        CUDA_IMPLICIT_GEMM_NCHW4_IMMA_INT8,
+        CUDA_IMPLICIT_GEMM_REORDER_FILTER_CHWN4_IMMA_INT8,
+        CUDA_IMPLICIT_GEMM_UNROLL_WIDTH_CHWN4_IMMA_INT8,
+        CUDA_IMPLICIT_GEMM_IMMA_NCHW32_INT8,
+        CUDA_BFLOAT16,
+        CUDA_IMPLICIT_GEMM_SASS_NCHW4_DOTPROD_INT8,
+        CUDA_IMPLICIT_GEMM_1X1_SASS_NCHW4_DOTPROD_INT8,
+        CUDA_IMPLICIT_GEMM_SASS_NCHW32_IMMA_INT8,
+        CUDA_IMPLICIT_GEMM_1X1_SASS_NCHW32_IMMA_INT8,
+    };
+    using Mapper = std::unordered_map<AlgorithmDesc, AlgoBase*>;
+
     AlgoBase() : Algorithm() { m_handle_type = Handle::HandleType::CUDA; }
     struct SizeArgs : public conv_bias::BiasForwardSizeArgs {
         ConvBiasForwardImpl* opr;
         const PreprocessedFilter* preprocessed_filter;
-        
+
         std::string to_string() const;
         SizeArgs(ConvBiasForwardImpl* opr, const TensorLayout& src,
                  const TensorLayout& filter, const TensorLayout& bias,
@@ -80,13 +112,17 @@ public:
     virtual void exec(const ExecArgs& args) const = 0;
     virtual size_t get_preprocess_workspace_in_bytes(
             const SizeArgs& args) const {
+        MEGDNN_MARK_USED_VAR(args);
         return 0;
     }
     virtual SmallVector<TensorLayout> deduce_preprocessed_filter_layout(
             const SizeArgs& args) const {
+        MEGDNN_MARK_USED_VAR(args);
         return {};
     }
-    virtual void exec_preprocess(const ExecArgs& args) const {}
+    virtual void exec_preprocess(const ExecArgs& args) const {
+        MEGDNN_MARK_USED_VAR(args);
+    }
 
     bool is_available_wk(const SizeArgs& args, size_t limit) {
         return is_available(args) && get_workspace_in_bytes(args) <= limit;
@@ -114,11 +150,14 @@ public:
 
 class ConvBiasForwardImpl::AlgoCUDNNConvBiasActivation final : public AlgoBase {
 public:
-    AlgoCUDNNConvBiasActivation(bool is_reproducible, const char* name,
-                                cudnnConvolutionFwdAlgo_t cudnn_enum)
-            : m_is_reproducible(is_reproducible),
-              m_name(ConvBiasForward::algo_name<DefaultParam>(name, {})),
-              m_cudnn_enum(cudnn_enum) {}
+    AlgoCUDNNConvBiasActivation(cudnnConvolutionFwdAlgo_t cudnn_enum)
+            : m_cudnn_enum(cudnn_enum) {
+        megdnn_assert(CudnnAlgoPack::conv_fwd_algos().find(cudnn_enum) !=
+                      CudnnAlgoPack::conv_fwd_algos().end());
+        m_attr = CudnnAlgoPack::conv_fwd_algos().at(cudnn_enum);
+        m_name = ConvBiasForward::algo_name<DefaultParam>(
+                "CUDNN:ConvBiasActivation:" + m_attr.name, {});
+    }
 
     size_t get_workspace_in_bytes(const SizeArgs& args) const override;
     void exec(const ExecArgs& args) const override;
@@ -127,16 +166,24 @@ public:
 
     const char* name() const override { return m_name.c_str(); }
 
-    bool is_reproducible() const override { return m_is_reproducible; }
+    bool is_reproducible() const override { return m_attr.is_reproducible; }
 
     cudnnConvolutionFwdAlgo_t cudnn_enum() { return m_cudnn_enum; }
 
     bool is_cudnn() const override { return true; }
 
+    MEGDNN_DECL_ALGO_TYPE(CUDA_CUDNN_CONVBIAS)
+
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_cudnn_enum, ret);
+        return ret;
+    }
+
 private:
-    bool m_is_reproducible;
     std::string m_name;
     cudnnConvolutionFwdAlgo_t m_cudnn_enum;
+    CudnnAlgoPack::Attr m_attr;
 };
 
 class ConvBiasForwardImpl::AlgoChanwise final : public AlgoBase {
@@ -154,6 +201,8 @@ public:
     }
     bool is_reproducible() const override { return true; }
 
+    MEGDNN_DECL_ALGO_TYPE(CUDA_CHANWISE)
+
 private:
     mutable std::string m_name;
 };
@@ -172,6 +221,7 @@ public:
         return m_name.c_str();
     }
     bool is_reproducible() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_CHANWISE_SMALL)
 
 private:
     mutable std::string m_name;
@@ -190,6 +240,7 @@ public:
         return m_name.c_str();
     }
     bool is_reproducible() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_CHANWISE_INT8X8X32)
 
 private:
     mutable std::string m_name;
@@ -197,27 +248,39 @@ private:
 
 class ConvBiasForwardImpl::AlgoCUDNNConv final : public AlgoBase {
 public:
-    AlgoCUDNNConv(bool is_reproducible, const char* name,
-                  cudnnConvolutionFwdAlgo_t cudnn_enum)
-            : m_is_reproducible(is_reproducible),
-              m_name(ConvBiasForward::algo_name<DefaultParam>(name, {})),
-              m_cudnn_enum(cudnn_enum) {}
+    AlgoCUDNNConv(cudnnConvolutionFwdAlgo_t cudnn_enum)
+            : m_cudnn_enum(cudnn_enum) {
+        megdnn_assert(CudnnAlgoPack::conv_fwd_algos().find(cudnn_enum) !=
+                      CudnnAlgoPack::conv_fwd_algos().end());
+        m_attr = CudnnAlgoPack::conv_fwd_algos().at(cudnn_enum);
+        m_name = ConvBiasForward::algo_name<DefaultParam>(
+                "CUDNN:Convolution:" + m_attr.name, {});
+    }
 
     bool is_available(const SizeArgs& args) const override;
     size_t get_workspace_in_bytes(const SizeArgs& args) const override;
     void exec(const ExecArgs& args) const override;
 
-    bool is_reproducible() const override { return m_is_reproducible; }
+    bool is_reproducible() const override { return m_attr.is_reproducible; }
 
     const char* name() const override { return m_name.c_str(); }
 
     cudnnConvolutionFwdAlgo_t cudnn_enum() const { return m_cudnn_enum; }
 
     bool is_cudnn() const override { return true; }
+
+    MEGDNN_DECL_ALGO_TYPE(CUDA_CUDNN_CONV)
+
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_cudnn_enum, ret);
+        return ret;
+    }
+
 private:
-    bool m_is_reproducible;
     std::string m_name;
     cudnnConvolutionFwdAlgo_t m_cudnn_enum;
+    CudnnAlgoPack::Attr m_attr;
 
     WorkspaceBundle get_workspace_bundle(void* ptr, const SizeArgs& args) const;
 };
@@ -237,6 +300,7 @@ public:
         return m_name.c_str();
     }
     bool is_reproducible() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_INPLACE_MATMUL)
 
 private:
     mutable std::string m_name;
@@ -261,6 +325,7 @@ public:
         return m_name.c_str();
     }
     bool is_reproducible() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_MATMUL)
 
 private:
     WorkspaceBundle get_workspace_bundle(void* ptr, const SizeArgs& args) const;
@@ -281,6 +346,7 @@ public:
         return m_name.c_str();
     }
     bool is_reproducible() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_MATMUL_INT8X8X32)
 
 private:
     bool need_src_unroll(const SizeArgs& args) const;
@@ -310,6 +376,7 @@ public:
         return m_name.c_str();
     }
     bool is_reproducible() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_1X1)
 
 private:
     WorkspaceBundle get_workspace_bundle(void* ptr, const SizeArgs& args) const;
@@ -333,6 +400,7 @@ public:
         return m_name.c_str();
     }
     bool is_reproducible() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_BATCHED_MATMUL)
 
 private:
     WorkspaceBundle get_workspace_bundle(void* ptr, const SizeArgs& args) const;
@@ -354,6 +422,13 @@ public:
 
     static void modify_size_args(SizeArgs& args, TensorLayout& src_pg,
                                  TensorLayout& dst_pg, TensorLayout& bias_pg);
+    MEGDNN_DECL_ALGO_TYPE(CUDA_GROUP_CONV_GENERAL)
+
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_impl, ret);
+        return ret;
+    }
 
 private:
     WorkspaceBundle get_workspace_bundle(void* ptr, const SizeArgs& args) const;
@@ -370,10 +445,13 @@ public:
     void exec(const ExecArgs& args) const override;
     const char* name() const override { return "QUINT4x4x32_WMMA"; }
     bool is_reproducible() const override { return true; }
+
 private:
-    WorkspaceBundle get_workspace_bundle(dt_byte* raw_ptr, const SizeArgs& args) const;
+    WorkspaceBundle get_workspace_bundle(dt_byte* raw_ptr,
+                                         const SizeArgs& args) const;
     bool use_kernel_fhxfw(const SizeArgs& args) const;
     size_t get_workspace_in_bytes_do_conv(const SizeArgs& args) const;
+    MEGDNN_DECL_ALGO_TYPE(CUDA_WMMA_UINT4X4X32)
 };
 #endif
 
@@ -395,6 +473,7 @@ public:
             const convolution::ConvParam& param, float alpha, float beta,
             float gamma, float scale, cudaStream_t stream,
             param::ConvBias::NonlineMode nonlinear_mode);
+    MEGDNN_DECL_ALGO_TYPE(CUDA_IMPLICIT_GEMM_CHWN4_DOTPROD_INT8)
 };
 
 class ConvBiasForwardImpl::AlgoInt8NCHW4DotProdImplicitGemm final
@@ -415,8 +494,9 @@ public:
                 warp_k == 32 && stage == 2) {
                 return "";
             }
-            return ssprintf("_%dX%dX%d_%dX%dX%d_%dstage", threadblock_m, threadblock_n,
-                            threadblock_k, warp_m, warp_n, warp_k, stage);
+            return ssprintf("_%dX%dX%d_%dX%dX%d_%dstage", threadblock_m,
+                            threadblock_n, threadblock_k, warp_m, warp_n,
+                            warp_k, stage);
         }
     };
     AlgoInt8NCHW4DotProdImplicitGemm(AlgoParam algo_param)
@@ -433,6 +513,13 @@ public:
     SmallVector<TensorLayout> deduce_preprocessed_filter_layout(
             const SizeArgs& args) const override;
     void exec_preprocess(const ExecArgs& args) const override;
+    MEGDNN_DECL_ALGO_TYPE(CUDA_IMPLICIT_GEMM_NCHW4_DOTPROD_INT8)
+
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_algo_param, ret);
+        return ret;
+    }
 
 private:
     WorkspaceBundle get_workspace_bundle(dt_byte* raw_ptr,
@@ -457,9 +544,7 @@ public:
     bool is_available(const SizeArgs& args) const override;
     size_t get_workspace_in_bytes(const SizeArgs& args) const override;
     void exec(const ExecArgs& args) const override;
-    const char* name() const override {
-        return m_name.c_str();
-    }
+    const char* name() const override { return m_name.c_str(); }
     bool is_reproducible() const override { return true; }
     template <typename BiasVisitor>
     static void dispatch_nonlinear_mode(
@@ -471,6 +556,14 @@ public:
             MMATileSize mma_tile_size);
     static std::string to_string(MMATileSize mma_tile_size);
 
+    MEGDNN_DECL_ALGO_TYPE(CUDA_IMPLICIT_GEMM_CHWN4_IMMA_INT8)
+
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_mma_tile_size, ret);
+        return ret;
+    }
+
 private:
     MMATileSize m_mma_tile_size;
     std::string m_name;
@@ -488,10 +581,16 @@ public:
     bool is_available(const SizeArgs& args) const override;
     size_t get_workspace_in_bytes(const SizeArgs& args) const override;
     void exec(const ExecArgs& args) const override;
-    const char* name() const override {
-        return m_name.c_str();
-    }
+    const char* name() const override { return m_name.c_str(); }
     bool is_reproducible() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_IMPLICIT_GEMM_NCHW4_IMMA_INT8)
+
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_mma_tile_size, ret);
+        return ret;
+    }
+
 private:
     WorkspaceBundle get_workspace_bundle(dt_byte* raw_ptr,
                                          const SizeArgs& args) const;
@@ -513,6 +612,13 @@ public:
     void exec(const ExecArgs& args) const override;
     const char* name() const override { return m_name.c_str(); }
     bool is_reproducible() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_IMPLICIT_GEMM_REORDER_FILTER_CHWN4_IMMA_INT8)
+
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_mma_tile_size, ret);
+        return ret;
+    }
 
 private:
     MMATileSize m_mma_tile_size;
@@ -533,6 +639,13 @@ public:
     void exec(const ExecArgs& args) const override;
     const char* name() const override { return m_name.c_str(); }
     bool is_reproducible() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_IMPLICIT_GEMM_UNROLL_WIDTH_CHWN4_IMMA_INT8)
+
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_mma_tile_size, ret);
+        return ret;
+    }
 
 private:
     MMATileSize m_mma_tile_size;
@@ -570,6 +683,13 @@ public:
     SmallVector<TensorLayout> deduce_preprocessed_filter_layout(
             const SizeArgs& args) const override;
     void exec_preprocess(const ExecArgs& args) const override;
+    MEGDNN_DECL_ALGO_TYPE(CUDA_IMPLICIT_GEMM_IMMA_NCHW32_INT8)
+
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_algo_param, ret);
+        return ret;
+    }
 
 private:
     WorkspaceBundle get_workspace_bundle(dt_byte* raw_ptr,
@@ -592,6 +712,14 @@ public:
 
     bool is_reproducible() const override { return m_impl->is_reproducible(); }
 
+    MEGDNN_DECL_ALGO_TYPE(CUDA_BFLOAT16)
+
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_impl, ret);
+        return ret;
+    }
+
 private:
     SizeArgs float_args(const SizeArgs& args, ConvBiasForwardImpl* opr,
                         TensorLayout& fsrc, TensorLayout& ffilter,
@@ -603,17 +731,16 @@ private:
 };
 
 
-class ConvBiasForwardImpl::AlgoPack {
-    AlgoPack(const AlgoPack&) = delete;
-    AlgoPack& operator=(const AlgoPack&) = delete;
+class ConvBiasForwardImpl::AlgoPack : NonCopyableObj {
+private:
+    AlgoBase::Mapper m_all_algos_map;
 
 public:
     AlgoPack();
 
     std::vector<AlgoBase*> all_algos,
             //! non-cudnn algos, used for heuristic if cudnn is not supported
-            non_cudnn_algos,
-            bfloat16_algos;
+            non_cudnn_algos, bfloat16_algos;
     std::vector<AlgoCUDNNConvBiasActivation> cudnn_conv_bias_activations;
     std::vector<AlgoCUDNNConv> cudnn_convs;
     AlgoChanwise chanwise;
@@ -646,6 +773,8 @@ public:
 
     AlgoBase* cudnn_conv_from_enum(cudnnConvolutionFwdAlgo_t algo);
 
+    const AlgoBase::Mapper& all_algos_map() const { return m_all_algos_map; }
+
 private:
 #if CUDA_VERSION >= 10000
     void fill_imma_algos();

dnn/src/cuda/conv_bias/bfloat16.cpp

@@ -47,7 +47,7 @@ ConvBiasForwardImpl::AlgoBFloat16::float_args(
     change_dtype(fdst);
     opr->param() = args.opr->param();
     opr->param().compute_mode = Param::ComputeMode::DEFAULT;
-    opr->execution_policy() = {m_impl};
+    opr->execution_policy() = {m_impl->info()};
     return SizeArgs(opr, fsrc, ffilter, fbias, fz, fdst);
 }
 
@@ -110,7 +110,7 @@ void ConvBiasForwardImpl::AlgoBFloat16::exec(const ExecArgs& args) const {
         auto convbias_opr = args.handle->create_operator<ConvBias>();
         convbias_opr->param() = args.opr->param();
         convbias_opr->param().compute_mode = Param::ComputeMode::DEFAULT;
-        convbias_opr->execution_policy() = {m_impl};
+        convbias_opr->execution_policy() = {m_impl->info()};
         convbias_opr->exec(fsrc_tensor, ffilter_tensor, fbias_tensor, fz_tensor,
                            fdst_tensor, nullptr, cvter.workspace());
     }

dnn/src/cuda/conv_bias/opr_impl.cpp

@@ -63,12 +63,12 @@ ConvBiasForward::Algorithm* ConvBiasForwardImpl::get_algorithm_heuristic(
     auto conv_args = args;
 
     auto cudnn_conv_bias_act_from_enum_wrapper =
-            [this](cudnnConvolutionFwdAlgo_t algo) -> AlgoBase* {
+            [](cudnnConvolutionFwdAlgo_t algo) -> AlgoBase* {
         return sm_algo_pack.cudnn_conv_bias_act_from_enum(algo);
     };
 
     auto cudnn_conv_from_enum_wrapper =
-            [this](cudnnConvolutionFwdAlgo_t algo) -> AlgoBase* {
+            [](cudnnConvolutionFwdAlgo_t algo) -> AlgoBase* {
         return sm_algo_pack.cudnn_conv_from_enum(algo);
     };
 

dnn/src/cuda/conv_bias/opr_impl.h

@@ -24,17 +24,6 @@ public:
               _megdnn_tensor_out dst,
               const PreprocessedFilter* preprocessed_filter,
               _megdnn_workspace workspace) override;
-    std::vector<Algorithm*> get_all_algorithms(
-            const TensorLayout& src, const TensorLayout& filter,
-            const TensorLayout& bias, const TensorLayout& z,
-            const TensorLayout& dst) override;
-    Algorithm* get_algorithm_heuristic(const TensorLayout& src,
-                                       const TensorLayout& filter,
-                                       const TensorLayout& bias,
-                                       const TensorLayout& z,
-                                       const TensorLayout& dst,
-                                       size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
     size_t get_workspace_in_bytes(const TensorLayout&, const TensorLayout&,
                                   const TensorLayout&, const TensorLayout&,
                                   const TensorLayout&,
@@ -80,6 +69,20 @@ public:
 
     static const AlgoPack& algo_pack() { return sm_algo_pack; }
 
+    static AlgoBase* get_algo_from_desc(const AlgorithmDesc& desc);
+
+    std::vector<Algorithm*> get_all_algorithms(
+            const TensorLayout& src, const TensorLayout& filter,
+            const TensorLayout& bias, const TensorLayout& z,
+            const TensorLayout& dst) override;
+    Algorithm* get_algorithm_heuristic(const TensorLayout& src,
+                                       const TensorLayout& filter,
+                                       const TensorLayout& bias,
+                                       const TensorLayout& z,
+                                       const TensorLayout& dst,
+                                       size_t workspace_limit_in_bytes,
+                                       bool reproducible) override;
+
 private:
     static AlgoPack sm_algo_pack;
 };

dnn/src/cuda/convolution/backward_data/algo.cpp

@@ -52,8 +52,14 @@ ConvolutionBackwardDataImpl::AlgoPack::AlgoPack() {
         all_algos.push_back(bfloat16_refhold.back().get());
         bfloat16_algos.push_back(bfloat16_refhold.back().get());
     }
+
+    for (auto&& algo : all_algos) {
+        m_all_algos_map.emplace(algo->info().desc, algo);
+    }
 }
 
+MEGDNN_DEF_GET_ALGO_FROM_DESC(ConvolutionBackwardDataImpl)
+
 ConvolutionBackwardDataImpl::AlgoCUDNN*
 ConvolutionBackwardDataImpl::AlgoPack::cudnn_from_enum(
         cudnnConvolutionBwdDataAlgo_t algo) {

dnn/src/cuda/convolution/backward_data/algo.h

@@ -11,8 +11,11 @@
 
 #pragma once
 
-#include "src/cuda/convolution/helper.h"
 #include <unordered_map>
+#include "src/common/algo_base.h"
+#include "src/common/metahelper.h"
+#include "src/cuda/convolution/helper.h"
+#include "src/cuda/cudnn_wrapper.h"
 
 namespace megdnn {
 namespace cuda {
@@ -23,154 +26,146 @@ namespace cuda {
  * All the algo impls should try to support non-contiguous batch dim, for group
  * conv execution.
  */
-class ConvolutionBackwardDataImpl::AlgoBase: public Algorithm {
-    protected:
-        ~AlgoBase() = default;
-
-    public:
-        AlgoBase() : Algorithm() { m_handle_type = Handle::HandleType::CUDA; }
-        struct SizeArgs {
-            HandleImpl *handle;
-            CanonizedFilterMeta filter_meta;
-            const TensorLayout *diff_layout, *grad_layout, *filter_layout;
-            ConvolutionBackwardDataImpl *opr;
-
-            std::string to_string() const;
-            void init_desc(convolution::CUDNNBwdDataDescs &desc) const {
-                desc.set(filter_meta, *diff_layout, *grad_layout, opr->param());
-            }
-            SizeArgs(ConvolutionBackwardDataImpl* opr,
-                     const TensorLayout& filter, const TensorLayout& diff,
-                     const TensorLayout& grad);
-            SizeArgs(ConvolutionBackwardDataImpl* opr,
-                     const TensorLayout& filter,
-                     const CanonizedFilterMeta& filter_meta,
-                     const TensorLayout& diff, const TensorLayout& grad);
-
-            convolution::ForwardSizeArgs as_fwd_args() const {
-                return {handle, grad_layout, filter_layout, filter_meta,
-                        diff_layout};
-            }
-        };
-        struct ExecArgs: public SizeArgs {
-            const TensorND *filter_tensor, *diff_tensor, *grad_tensor;
-            Workspace workspace;
-
-            ExecArgs(ConvolutionBackwardDataImpl *opr,
-                    _megdnn_tensor_in filter,
-                    _megdnn_tensor_in diff,
-                    _megdnn_tensor_out grad,
-                    _megdnn_workspace workspace);
-        };
-        virtual bool is_available(const SizeArgs &args) const = 0;
-        virtual size_t get_workspace_in_bytes(const SizeArgs &args) const = 0;
-        virtual void exec(const ExecArgs &args) const = 0;
-
-        bool is_available_wk(const SizeArgs &args, size_t limit) {
-            return is_available(args) && get_workspace_in_bytes(args) <= limit;
-        }
+class ConvolutionBackwardDataImpl::AlgoBase : public Algorithm {
+protected:
+    ~AlgoBase() = default;
 
-        bool is_available_reproducible(
-                const SizeArgs& args, bool reproducible = true,
-                size_t limit = std::numeric_limits<size_t>::max()) {
-            return (!reproducible || is_reproducible()) &&
-                   is_available_wk(args, limit);
-        }
-
-        AlgoBase& check_workspace(
-                const SizeArgs &args, const Workspace &workspace) {
-            auto req = get_workspace_in_bytes(args);
-            megdnn_assert(req <= workspace.size,
-                    "conv bwd data algo %s: "
-                    "required workspace %zu bytes, got %zu",
-                    name(), req, workspace.size);
-            return *this;
+public:
+    enum class AlgoType : uint32_t {
+        CUDA_CUDNN,
+        CUDA_MATMUL,
+        CUDA_CHANWISE,
+        CUDA_CHANWISE_SMALL,
+        CUDA_BFLOAT16,
+        CUDA_GROUP_CONV_GENERAL,
+    };
+    using Mapper = std::unordered_map<AlgorithmDesc, AlgoBase*>;
+
+    AlgoBase() : Algorithm() { m_handle_type = Handle::HandleType::CUDA; }
+    struct SizeArgs {
+        HandleImpl* handle;
+        CanonizedFilterMeta filter_meta;
+        const TensorLayout *diff_layout, *grad_layout, *filter_layout;
+        ConvolutionBackwardDataImpl* opr;
+
+        std::string to_string() const;
+        void init_desc(convolution::CUDNNBwdDataDescs& desc) const {
+            desc.set(filter_meta, *diff_layout, *grad_layout, opr->param());
         }
-
-        virtual bool is_cudnn() const {
-            return false;
+        SizeArgs(ConvolutionBackwardDataImpl* opr, const TensorLayout& filter,
+                 const TensorLayout& diff, const TensorLayout& grad);
+        SizeArgs(ConvolutionBackwardDataImpl* opr, const TensorLayout& filter,
+                 const CanonizedFilterMeta& filter_meta,
+                 const TensorLayout& diff, const TensorLayout& grad);
+
+        convolution::ForwardSizeArgs as_fwd_args() const {
+            return {handle, grad_layout, filter_layout, filter_meta,
+                    diff_layout};
         }
+    };
+    struct ExecArgs : public SizeArgs {
+        const TensorND *filter_tensor, *diff_tensor, *grad_tensor;
+        Workspace workspace;
+
+        ExecArgs(ConvolutionBackwardDataImpl* opr, _megdnn_tensor_in filter,
+                 _megdnn_tensor_in diff, _megdnn_tensor_out grad,
+                 _megdnn_workspace workspace);
+    };
+    virtual bool is_available(const SizeArgs& args) const = 0;
+    virtual size_t get_workspace_in_bytes(const SizeArgs& args) const = 0;
+    virtual void exec(const ExecArgs& args) const = 0;
+
+    bool is_available_wk(const SizeArgs& args, size_t limit) {
+        return is_available(args) && get_workspace_in_bytes(args) <= limit;
+    }
+
+    bool is_available_reproducible(
+            const SizeArgs& args, bool reproducible = true,
+            size_t limit = std::numeric_limits<size_t>::max()) {
+        return (!reproducible || is_reproducible()) &&
+               is_available_wk(args, limit);
+    }
+
+    AlgoBase& check_workspace(const SizeArgs& args,
+                              const Workspace& workspace) {
+        auto req = get_workspace_in_bytes(args);
+        megdnn_assert(req <= workspace.size,
+                      "conv bwd data algo %s: "
+                      "required workspace %zu bytes, got %zu",
+                      name(), req, workspace.size);
+        return *this;
+    }
+
+    virtual bool is_cudnn() const { return false; }
 };
 
 class ConvolutionBackwardDataImpl::AlgoCUDNN final : public AlgoBase {
-    bool m_is_reproducible;
-    const char *m_name;
     cudnnConvolutionBwdDataAlgo_t m_cudnn_enum;
+    CudnnAlgoPack::Attr m_attr;
 
-    public:
+public:
+    AlgoCUDNN(cudnnConvolutionBwdDataAlgo_t cudnn_enum)
+            : m_cudnn_enum(cudnn_enum) {
+        megdnn_assert(CudnnAlgoPack::conv_bwd_data_algos().find(cudnn_enum) !=
+                      CudnnAlgoPack::conv_bwd_data_algos().end());
+        m_attr = CudnnAlgoPack::conv_bwd_data_algos().at(cudnn_enum);
+    }
 
-        AlgoCUDNN(bool is_reproducible, const char *name,
-                cudnnConvolutionBwdDataAlgo_t cudnn_enum):
-            m_is_reproducible(is_reproducible),
-            m_name(name),
-            m_cudnn_enum(cudnn_enum)
-        {}
+    bool is_available(const SizeArgs& args) const override;
+    size_t get_workspace_in_bytes(const SizeArgs& args) const override;
+    void exec(const ExecArgs& args) const override;
 
-        bool is_available(const SizeArgs &args) const override;
-        size_t get_workspace_in_bytes(const SizeArgs &args) const override;
-        void exec(const ExecArgs &args) const override;
+    bool is_reproducible() const override { return m_attr.is_reproducible; }
 
-        bool is_reproducible() const override {
-            return m_is_reproducible;
-        }
+    const char* name() const override { return m_attr.name.c_str(); }
 
-        const char* name() const override {
-            return m_name;
-        }
+    cudnnConvolutionBwdDataAlgo_t cudnn_enum() const { return m_cudnn_enum; }
 
-        cudnnConvolutionBwdDataAlgo_t cudnn_enum() const {
-            return m_cudnn_enum;
-        }
+    bool is_cudnn() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_CUDNN)
 
-        bool is_cudnn() const override {
-            return true;
-        }
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_cudnn_enum, ret);
+        return ret;
+    }
 };
 
 //! im2col and matmul, with dilation
-class ConvolutionBackwardDataImpl::AlgoMatmul final: public AlgoBase {
-    template<typename T>
-    static void exec_internal(const ExecArgs &args);
+class ConvolutionBackwardDataImpl::AlgoMatmul final : public AlgoBase {
+    template <typename T>
+    static void exec_internal(const ExecArgs& args);
 
-    public:
-        bool is_available(const SizeArgs &args) const override;
-        size_t get_workspace_in_bytes(const SizeArgs &args) const override;
-        void exec(const ExecArgs &args) const override;
+public:
+    bool is_available(const SizeArgs& args) const override;
+    size_t get_workspace_in_bytes(const SizeArgs& args) const override;
+    void exec(const ExecArgs& args) const override;
 
-        const char* name() const override {
-            return "MATMUL";
-        }
-        bool is_reproducible() const override {
-            return true;
-        }
+    const char* name() const override { return "MATMUL"; }
+    bool is_reproducible() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_MATMUL)
 };
 
-class ConvolutionBackwardDataImpl::AlgoChanwise final: public AlgoBase {
-    public:
-        bool is_available(const SizeArgs &args) const override;
-        size_t get_workspace_in_bytes(const SizeArgs &args) const override;
-        void exec(const ExecArgs &args) const override;
+class ConvolutionBackwardDataImpl::AlgoChanwise final : public AlgoBase {
+public:
+    bool is_available(const SizeArgs& args) const override;
+    size_t get_workspace_in_bytes(const SizeArgs& args) const override;
+    void exec(const ExecArgs& args) const override;
 
-        const char* name() const override {
-            return "CHANNEL_WISE";
-        }
-        bool is_reproducible() const override {
-            return true;
-        }
+    const char* name() const override { return "CHANNEL_WISE"; }
+    bool is_reproducible() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_CHANWISE)
 };
 
-class ConvolutionBackwardDataImpl::AlgoChanwiseSmall final: public AlgoBase {
-    public:
-        bool is_available(const SizeArgs &args) const override;
-        size_t get_workspace_in_bytes(const SizeArgs &args) const override;
-        void exec(const ExecArgs &args) const override;
+class ConvolutionBackwardDataImpl::AlgoChanwiseSmall final : public AlgoBase {
+public:
+    bool is_available(const SizeArgs& args) const override;
+    size_t get_workspace_in_bytes(const SizeArgs& args) const override;
+    void exec(const ExecArgs& args) const override;
 
-        const char* name() const override {
-            return "CHANNEL_WISE_SMALL";
-        }
-        bool is_reproducible() const override {
-            return true;
-        }
+    const char* name() const override { return "CHANNEL_WISE_SMALL"; }
+    bool is_reproducible() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_CHANWISE_SMALL)
 };
 
 class ConvolutionBackwardDataImpl::AlgoBFloat16 final : public AlgoBase {
@@ -190,61 +185,72 @@ private:
                         TensorLayout& fsrc, TensorLayout& ffilter,
                         TensorLayout& fdst) const;
     WorkspaceBundle get_workspace_bundle(void* ptr, const SizeArgs& args) const;
+    MEGDNN_DECL_ALGO_TYPE(CUDA_BFLOAT16)
+
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_algorithm, ret);
+        return ret;
+    }
 };
 
 //! implement group conv by another algo
-class ConvolutionBackwardDataImpl::AlgoGroupConvGeneral final: public AlgoBase {
-    AlgoBase *m_impl;
+class ConvolutionBackwardDataImpl::AlgoGroupConvGeneral final
+        : public AlgoBase {
+    AlgoBase* m_impl;
     std::string m_name;
 
-    public:
-        AlgoGroupConvGeneral(AlgoBase *impl);
+public:
+    AlgoGroupConvGeneral(AlgoBase* impl);
 
-        bool is_available(const SizeArgs &args) const override;
-        size_t get_workspace_in_bytes(const SizeArgs &args) const override;
-        void exec(const ExecArgs &args) const override;
+    bool is_available(const SizeArgs& args) const override;
+    size_t get_workspace_in_bytes(const SizeArgs& args) const override;
+    void exec(const ExecArgs& args) const override;
 
-        const char* name() const override {
-            return m_name.c_str();
-        }
+    const char* name() const override { return m_name.c_str(); }
 
-        bool is_reproducible() const override {
-            return m_impl->is_reproducible();
-        }
+    bool is_reproducible() const override { return m_impl->is_reproducible(); }
+
+    static void modify_size_args(SizeArgs& args, TensorLayout& diff_pg,
+                                 TensorLayout& grad_pg);
+    MEGDNN_DECL_ALGO_TYPE(CUDA_GROUP_CONV_GENERAL)
 
-        static void modify_size_args(SizeArgs &args,
-                TensorLayout &diff_pg, TensorLayout &grad_pg);
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_impl, ret);
+        return ret;
+    }
 };
 
-class ConvolutionBackwardDataImpl::AlgoPack {
+class ConvolutionBackwardDataImpl::AlgoPack : NonCopyableObj {
     // defined in cudnn.cpp
     void fill_cudnn_algos();
 
-    AlgoPack(const AlgoPack&) = delete;
-    AlgoPack& operator = (const AlgoPack &) = delete;
+    AlgoBase::Mapper m_all_algos_map;
 
-    public:
-        AlgoPack();
+public:
+    AlgoPack();
 
-        std::vector<AlgoCUDNN> cudnn;
-        AlgoMatmul matmul;
-        AlgoChanwise chanwise;
-        AlgoChanwiseSmall chanwise_small;
-        std::vector<AlgoGroupConvGeneral> gconv;
-        std::unordered_map<AlgoBase*, AlgoGroupConvGeneral*> algo2gconv;
-        std::vector<std::unique_ptr<AlgoBFloat16>> bfloat16_refhold;
+    std::vector<AlgoCUDNN> cudnn;
+    AlgoMatmul matmul;
+    AlgoChanwise chanwise;
+    AlgoChanwiseSmall chanwise_small;
+    std::vector<AlgoGroupConvGeneral> gconv;
+    std::unordered_map<AlgoBase*, AlgoGroupConvGeneral*> algo2gconv;
+    std::vector<std::unique_ptr<AlgoBFloat16>> bfloat16_refhold;
 
-        std::vector<AlgoBase*>
+    std::vector<AlgoBase*>
             //! all algorithms
             all_algos,
             //! non-cudnn algos, used for heuristic if cudnn is not supported
-            non_cudnn_algos,
-            bfloat16_algos;
+            non_cudnn_algos, bfloat16_algos;
+
+    AlgoCUDNN* cudnn_from_enum(cudnnConvolutionBwdDataAlgo_t algo);
 
-        AlgoCUDNN* cudnn_from_enum(cudnnConvolutionBwdDataAlgo_t algo);
+    const AlgoBase::Mapper& all_algos_map() const { return m_all_algos_map; }
 };
 
-} // namespace cuda
-} // namespace megdnn
+}  // namespace cuda
+}  // namespace megdnn
 
 // vim: syntax=cpp.doxygen

dnn/src/cuda/convolution/backward_data/bfloat16.cpp

@@ -42,7 +42,7 @@ ConvolutionBackwardDataImpl::AlgoBFloat16::float_args(
     change_dtype(fgrad);
     opr->param() = args.opr->param();
     opr->param().compute_mode = Param::ComputeMode::DEFAULT;
-    opr->execution_policy() = {m_algorithm};
+    opr->execution_policy() = {m_algorithm->info()};
     return SizeArgs(opr, ffilter, fdiff, fgrad);
 }
 
@@ -105,7 +105,7 @@ void ConvolutionBackwardDataImpl::AlgoBFloat16::exec(
                 args.handle->create_operator<ConvolutionBackwardData>();
         conv_back_data_opr->param() = args.opr->param();
         conv_back_data_opr->param().compute_mode = Param::ComputeMode::DEFAULT;
-        conv_back_data_opr->execution_policy() = {m_algorithm};
+        conv_back_data_opr->execution_policy() = {m_algorithm->info()};
         conv_back_data_opr->exec(ffilter_tensor, fdiff_tensor, fgrad_tensor,
                                  cvter.workspace());
     }

dnn/src/cuda/convolution/backward_data/cudnn.cpp

@@ -98,35 +98,9 @@ void ConvolutionBackwardDataImpl::AlgoCUDNN::exec(
 }
 
 void ConvolutionBackwardDataImpl::AlgoPack::fill_cudnn_algos() {
-#define V1(v) #v
-#define V(v) V1(v)
-
-#define DEF_ALGO(NAME, REPROD) \
-    cudnn.push_back({ \
-                REPROD, #NAME \
-                    "v" V(CUDNN_MAJOR) "." V(CUDNN_MINOR) \
-                    "." V(CUDNN_PATCHLEVEL), \
-                NAME})
-
-    DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_0, false);
-    DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_1, true);
-    DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT, true);
-    DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT_TILING, true);
-#if CUDNN_MAJOR >= 5
-    DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD, true);
-#if CUDNN_MAJOR >= 6 || CUDNN_MINOR >= 1
-    DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED, true);
-#endif
-#endif
-
-#if !(CUDNN_MAJOR >= 6 || CUDNN_MINOR >= 1)
-#pragma message "not latest cudnn"
-#endif
-
-#undef DEF_ALGO
-
-#undef V
-#undef V1
+    for (auto&& algo : CudnnAlgoPack::conv_bwd_data_algos()) {
+        cudnn.push_back(algo.first);
+    }
 }
 
 // vim: syntax=cpp.doxygen

dnn/src/cuda/convolution/backward_filter/algo.cpp

@@ -49,8 +49,14 @@ ConvolutionBackwardFilterImpl::AlgoPack::AlgoPack() {
         all_algos.push_back(bfloat16_refhold.back().get());
         bfloat16_algos.push_back(bfloat16_refhold.back().get());
     }
+
+    for (auto&& algo : all_algos) {
+        m_all_algos_map.emplace(algo->info().desc, algo);
+    }
 }
 
+MEGDNN_DEF_GET_ALGO_FROM_DESC(ConvolutionBackwardFilterImpl)
+
 ConvolutionBackwardFilterImpl::AlgoCUDNN*
 ConvolutionBackwardFilterImpl::AlgoPack::cudnn_from_enum(
         cudnnConvolutionBwdFilterAlgo_t algo) {

dnn/src/cuda/convolution/backward_filter/algo.h

@@ -6,13 +6,16 @@
  *
  * 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.
  */
 
 #pragma once
 
-#include "src/cuda/convolution/helper.h"
 #include <unordered_map>
+#include "src/common/algo_base.h"
+#include "src/common/metahelper.h"
+#include "src/cuda/convolution/helper.h"
 
 namespace megdnn {
 namespace cuda {
@@ -23,141 +26,134 @@ namespace cuda {
  * All the algo impls should try to support non-contiguous batch dim, for group
  * conv execution.
  */
-class ConvolutionBackwardFilterImpl::AlgoBase: public Algorithm {
-    protected:
-        ~AlgoBase() = default;
-
-    public:
-        AlgoBase() : Algorithm() { m_handle_type = Handle::HandleType::CUDA; }
-        struct SizeArgs {
-            HandleImpl *handle;
-            const TensorLayout *src_layout, *diff_layout, *grad_layout;
-            CanonizedFilterMeta grad_filter_meta;
-            ConvolutionBackwardFilterImpl *opr;
-
-            std::string to_string() const;
-            void init_desc(convolution::CUDNNBwdFilterDescs &desc) const {
-                desc.set(*src_layout, *diff_layout, grad_filter_meta,
-                        opr->param());
-            }
-            SizeArgs(ConvolutionBackwardFilterImpl *opr,
-                    const TensorLayout &src, const TensorLayout &diff,
-                    const TensorLayout &grad);
-            SizeArgs(ConvolutionBackwardFilterImpl* opr,
-                     const TensorLayout& src, const TensorLayout& diff,
-                     const TensorLayout& grad,
-                     const CanonizedFilterMeta& grad_meta);
-
-            convolution::ForwardSizeArgs as_fwd_args() const {
-                return {handle, src_layout, grad_layout, grad_filter_meta,
-                        diff_layout};
-            }
-        };
-        struct ExecArgs: public SizeArgs {
-            const TensorND *src_tensor, *diff_tensor, *grad_tensor;
-            Workspace workspace;
-
-            ExecArgs(ConvolutionBackwardFilterImpl *opr,
-                    _megdnn_tensor_in src,
-                    _megdnn_tensor_in diff,
-                    _megdnn_tensor_out grad,
-                    _megdnn_workspace workspace);
-        };
-        virtual bool is_available(const SizeArgs &args) const = 0;
-        virtual size_t get_workspace_in_bytes(const SizeArgs &args) const = 0;
-        virtual void exec(const ExecArgs &args) const = 0;
-
-        bool is_available_wk(const SizeArgs &args, size_t limit) {
-            return is_available(args) && get_workspace_in_bytes(args) <= limit;
-        }
+class ConvolutionBackwardFilterImpl::AlgoBase : public Algorithm {
+protected:
+    ~AlgoBase() = default;
 
-        bool is_available_reproducible(
-                const SizeArgs& args, bool reproducible = true,
-                size_t limit = std::numeric_limits<size_t>::max()) {
-            return (!reproducible || is_reproducible()) &&
-                   is_available_wk(args, limit);
+public:
+    enum class AlgoType : uint32_t {
+        CUDA_CUDNN,
+        CUDA_MATMUL,
+        CUDA_CHANWISE,
+        CUDA_BFLOAT16,
+        CUDA_GROUP_CONV_GENERAL,
+    };
+    using Mapper = std::unordered_map<AlgorithmDesc, AlgoBase*>;
+
+    AlgoBase() : Algorithm() { m_handle_type = Handle::HandleType::CUDA; }
+    struct SizeArgs {
+        HandleImpl* handle;
+        const TensorLayout *src_layout, *diff_layout, *grad_layout;
+        CanonizedFilterMeta grad_filter_meta;
+        ConvolutionBackwardFilterImpl* opr;
+
+        std::string to_string() const;
+        void init_desc(convolution::CUDNNBwdFilterDescs& desc) const {
+            desc.set(*src_layout, *diff_layout, grad_filter_meta, opr->param());
         }
-
-        AlgoBase& check_workspace(
-                const SizeArgs &args, const Workspace &workspace) {
-            auto req = get_workspace_in_bytes(args);
-            megdnn_assert(req <= workspace.size,
-                    "conv bwd filter algo %s: "
-                    "required workspace %zu bytes, got %zu",
-                    name(), req, workspace.size);
-            return *this;
-        }
-
-        virtual bool is_cudnn() const {
-            return false;
+        SizeArgs(ConvolutionBackwardFilterImpl* opr, const TensorLayout& src,
+                 const TensorLayout& diff, const TensorLayout& grad);
+        SizeArgs(ConvolutionBackwardFilterImpl* opr, const TensorLayout& src,
+                 const TensorLayout& diff, const TensorLayout& grad,
+                 const CanonizedFilterMeta& grad_meta);
+
+        convolution::ForwardSizeArgs as_fwd_args() const {
+            return {handle, src_layout, grad_layout, grad_filter_meta,
+                    diff_layout};
         }
+    };
+    struct ExecArgs : public SizeArgs {
+        const TensorND *src_tensor, *diff_tensor, *grad_tensor;
+        Workspace workspace;
+
+        ExecArgs(ConvolutionBackwardFilterImpl* opr, _megdnn_tensor_in src,
+                 _megdnn_tensor_in diff, _megdnn_tensor_out grad,
+                 _megdnn_workspace workspace);
+    };
+    virtual bool is_available(const SizeArgs& args) const = 0;
+    virtual size_t get_workspace_in_bytes(const SizeArgs& args) const = 0;
+    virtual void exec(const ExecArgs& args) const = 0;
+
+    bool is_available_wk(const SizeArgs& args, size_t limit) {
+        return is_available(args) && get_workspace_in_bytes(args) <= limit;
+    }
+
+    bool is_available_reproducible(
+            const SizeArgs& args, bool reproducible = true,
+            size_t limit = std::numeric_limits<size_t>::max()) {
+        return (!reproducible || is_reproducible()) &&
+               is_available_wk(args, limit);
+    }
+
+    AlgoBase& check_workspace(const SizeArgs& args,
+                              const Workspace& workspace) {
+        auto req = get_workspace_in_bytes(args);
+        megdnn_assert(req <= workspace.size,
+                      "conv bwd filter algo %s: "
+                      "required workspace %zu bytes, got %zu",
+                      name(), req, workspace.size);
+        return *this;
+    }
+
+    virtual bool is_cudnn() const { return false; }
 };
 
 class ConvolutionBackwardFilterImpl::AlgoCUDNN final : public AlgoBase {
-    bool m_is_reproducible;
-    const char *m_name;
     cudnnConvolutionBwdFilterAlgo_t m_cudnn_enum;
+    CudnnAlgoPack::Attr m_attr;
 
-    public:
+public:
+    AlgoCUDNN(cudnnConvolutionBwdFilterAlgo_t cudnn_enum)
+            : m_cudnn_enum(cudnn_enum) {
+        megdnn_assert(CudnnAlgoPack::conv_bwd_flt_algos().find(cudnn_enum) !=
+                      CudnnAlgoPack::conv_bwd_flt_algos().end());
+        m_attr = CudnnAlgoPack::conv_bwd_flt_algos().at(cudnn_enum);
+    }
 
-        AlgoCUDNN(bool is_reproducible, const char *name,
-                cudnnConvolutionBwdFilterAlgo_t cudnn_enum):
-            m_is_reproducible(is_reproducible),
-            m_name(name),
-            m_cudnn_enum(cudnn_enum)
-        {}
+    bool is_available(const SizeArgs& args) const override;
+    size_t get_workspace_in_bytes(const SizeArgs& args) const override;
+    void exec(const ExecArgs& args) const override;
 
-        bool is_available(const SizeArgs &args) const override;
-        size_t get_workspace_in_bytes(const SizeArgs &args) const override;
-        void exec(const ExecArgs &args) const override;
+    bool is_reproducible() const override { return m_attr.is_reproducible; }
 
-        bool is_reproducible() const override {
-            return m_is_reproducible;
-        }
+    const char* name() const override { return m_attr.name.c_str(); }
 
-        const char* name() const override {
-            return m_name;
-        }
+    cudnnConvolutionBwdFilterAlgo_t cudnn_enum() const { return m_cudnn_enum; }
 
-        cudnnConvolutionBwdFilterAlgo_t cudnn_enum() const {
-            return m_cudnn_enum;
-        }
+    bool is_cudnn() const override { return true; }
 
-        bool is_cudnn() const override {
-            return true;
-        }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_CUDNN)
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_cudnn_enum, ret);
+        return ret;
+    }
 };
 
 //! im2col and matmul, with dilation
-class ConvolutionBackwardFilterImpl::AlgoMatmul final: public AlgoBase {
-    template<typename T>
-    static void exec_internal(const ExecArgs &args);
+class ConvolutionBackwardFilterImpl::AlgoMatmul final : public AlgoBase {
+    template <typename T>
+    static void exec_internal(const ExecArgs& args);
 
-    public:
-        bool is_available(const SizeArgs &args) const override;
-        size_t get_workspace_in_bytes(const SizeArgs &args) const override;
-        void exec(const ExecArgs &args) const override;
+public:
+    bool is_available(const SizeArgs& args) const override;
+    size_t get_workspace_in_bytes(const SizeArgs& args) const override;
+    void exec(const ExecArgs& args) const override;
 
-        const char* name() const override {
-            return "MATMUL";
-        }
-        bool is_reproducible() const override {
-            return true;
-        }
+    const char* name() const override { return "MATMUL"; }
+    bool is_reproducible() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_MATMUL)
 };
 
-class ConvolutionBackwardFilterImpl::AlgoChanwise final: public AlgoBase {
-    public:
-        bool is_available(const SizeArgs &args) const override;
-        size_t get_workspace_in_bytes(const SizeArgs &args) const override;
-        void exec(const ExecArgs &args) const override;
+class ConvolutionBackwardFilterImpl::AlgoChanwise final : public AlgoBase {
+public:
+    bool is_available(const SizeArgs& args) const override;
+    size_t get_workspace_in_bytes(const SizeArgs& args) const override;
+    void exec(const ExecArgs& args) const override;
 
-        const char* name() const override {
-            return "CHANNEL_WISE";
-        }
-        bool is_reproducible() const override {
-            return true;
-        }
+    const char* name() const override { return "CHANNEL_WISE"; }
+    bool is_reproducible() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_CHANWISE)
 };
 
 class ConvolutionBackwardFilterImpl::AlgoBFloat16 final : public AlgoBase {
@@ -169,6 +165,13 @@ public:
 
     const char* name() const override { return m_name.c_str(); }
     bool is_reproducible() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_BFLOAT16)
+
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_algorithm, ret);
+        return ret;
+    }
 
 private:
     std::string m_name;
@@ -180,57 +183,62 @@ private:
 };
 
 //! implement group conv by another algo
-class ConvolutionBackwardFilterImpl::AlgoGroupConvGeneral final: public AlgoBase {
-    AlgoBase *m_impl;
+class ConvolutionBackwardFilterImpl::AlgoGroupConvGeneral final
+        : public AlgoBase {
+    AlgoBase* m_impl;
     std::string m_name;
 
-    public:
-        AlgoGroupConvGeneral(AlgoBase *impl);
+public:
+    AlgoGroupConvGeneral(AlgoBase* impl);
+
+    bool is_available(const SizeArgs& args) const override;
+    size_t get_workspace_in_bytes(const SizeArgs& args) const override;
+    void exec(const ExecArgs& args) const override;
+
+    const char* name() const override { return m_name.c_str(); }
 
-        bool is_available(const SizeArgs &args) const override;
-        size_t get_workspace_in_bytes(const SizeArgs &args) const override;
-        void exec(const ExecArgs &args) const override;
+    bool is_reproducible() const override { return m_impl->is_reproducible(); }
 
-        const char* name() const override {
-            return m_name.c_str();
-        }
+    static void modify_size_args(SizeArgs& args, TensorLayout& src_pg,
+                                 TensorLayout& diff_pg);
 
-        bool is_reproducible() const override {
-            return m_impl->is_reproducible();
-        }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_GROUP_CONV_GENERAL)
 
-        static void modify_size_args(SizeArgs &args,
-                TensorLayout &src_pg, TensorLayout &diff_pg);
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_impl, ret);
+        return ret;
+    }
 };
 
-class ConvolutionBackwardFilterImpl::AlgoPack {
+class ConvolutionBackwardFilterImpl::AlgoPack : NonCopyableObj {
     // defined in cudnn.cpp
     void fill_cudnn_algos();
 
-    AlgoPack(const AlgoPack&) = delete;
-    AlgoPack& operator = (const AlgoPack &) = delete;
+    AlgoBase::Mapper m_all_algos_map;
 
-    public:
-        AlgoPack();
+public:
+    AlgoPack();
 
-        std::vector<AlgoCUDNN> cudnn;
-        AlgoMatmul matmul;
-        AlgoChanwise chanwise;
-        std::vector<AlgoGroupConvGeneral> gconv;
-        std::unordered_map<AlgoBase*, AlgoGroupConvGeneral*> algo2gconv;
-        std::vector<std::unique_ptr<AlgoBFloat16>> bfloat16_refhold;
+    std::vector<AlgoCUDNN> cudnn;
+    AlgoMatmul matmul;
+    AlgoChanwise chanwise;
+    std::vector<AlgoGroupConvGeneral> gconv;
+    std::unordered_map<AlgoBase*, AlgoGroupConvGeneral*> algo2gconv;
+    std::vector<std::unique_ptr<AlgoBFloat16>> bfloat16_refhold;
 
-        std::vector<AlgoBase*>
+    std::vector<AlgoBase*>
             //! all algorithms
             all_algos,
             //! non-cudnn algos, used for heuristic if cudnn is not supported
-            non_cudnn_algos,
-            bfloat16_algos;
+            non_cudnn_algos, bfloat16_algos;
+
+    AlgoCUDNN* cudnn_from_enum(cudnnConvolutionBwdFilterAlgo_t algo);
 
-        AlgoCUDNN* cudnn_from_enum(cudnnConvolutionBwdFilterAlgo_t algo);
+    const AlgoBase::Mapper& all_algos_map() const { return m_all_algos_map; }
 };
 
-} // namespace cuda
-} // namespace megdnn
+}  // namespace cuda
+}  // namespace megdnn
 
 // vim: syntax=cpp.doxygen

dnn/src/cuda/convolution/backward_filter/bfloat16.cpp

@@ -42,7 +42,7 @@ ConvolutionBackwardFilterImpl::AlgoBFloat16::float_args(
     change_dtype(fgrad);
     opr->param() = args.opr->param();
     opr->param().compute_mode = Param::ComputeMode::DEFAULT;
-    opr->execution_policy() = {m_algorithm};
+    opr->execution_policy() = {m_algorithm->info()};
     return SizeArgs(opr, fsrc, fdiff, fgrad);
 }
 
@@ -107,7 +107,7 @@ void ConvolutionBackwardFilterImpl::AlgoBFloat16::exec(
         conv_back_filter_opr->param() = args.opr->param();
         conv_back_filter_opr->param().compute_mode =
                 Param::ComputeMode::DEFAULT;
-        conv_back_filter_opr->execution_policy() = {m_algorithm};
+        conv_back_filter_opr->execution_policy() = {m_algorithm->info()};
         conv_back_filter_opr->exec(fsrc_tensor, fdiff_tensor, fgrad_tensor,
                                    cvter.workspace());
     }

dnn/src/cuda/convolution/backward_filter/cudnn.cpp

@@ -80,35 +80,9 @@ void ConvolutionBackwardFilterImpl::AlgoCUDNN::exec(
 }
 
 void ConvolutionBackwardFilterImpl::AlgoPack::fill_cudnn_algos() {
-#define V1(v) #v
-#define V(v) V1(v)
-
-#define DEF_ALGO(NAME, REPROD) \
-    cudnn.push_back({ \
-                REPROD, #NAME \
-                    "v" V(CUDNN_MAJOR) "." V(CUDNN_MINOR) \
-                    "." V(CUDNN_PATCHLEVEL), \
-                NAME})
-
-    DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0, false);
-    DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1, true);
-    DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT, true);
-    DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_3, false);
-#if CUDNN_MAJOR >= 6 || (CUDNN_MAJOR >= 5 && CUDNN_MINOR >= 1)
-    DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED, true);
-#if CUDNN_MAJOR >= 6
-    DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT_TILING, true);
-#endif
-#endif
-
-#if !(CUDNN_MAJOR >= 6 || CUDNN_MINOR >= 1)
-#pragma message "not latest cudnn"
-#endif
-
-#undef DEF_ALGO
-
-#undef V
-#undef V1
+    for(auto&& algo : CudnnAlgoPack::conv_bwd_flt_algos()) {
+        cudnn.push_back(algo.first);
+    }
 }
 
 // vim: syntax=cpp.doxygen

dnn/src/cuda/convolution/opr_impl.cpp

@@ -70,7 +70,7 @@ ConvolutionForwardImpl::conv_bias_extra_data(const TensorLayout& src,
                                  conv_param.dilate_w,
                                  0,
                                  conv_param.compute_mode};
-    ret.convbias_opr->execution_policy() = {this->execution_policy().algorithm};
+    ret.convbias_opr->execution_policy() = {this->execution_policy().algo};
     return ret;
 }
 
@@ -183,15 +183,6 @@ ConvolutionBackwardDataImpl::get_algorithm_heuristic(const TensorLayout& filter,
         CUDNNBwdDataDescs desc;
         args.init_desc(desc);
 
-        //disable, segfault in megbrain, need further investigate.
-#if 0
-        bool is_heuristic_success= convolution::
-                PerformanceModelBackwardData::get_algo_backward_data_success(
-                        args, desc, workspace_limit_in_bytes, &algo);
-        if (is_heuristic_success) {
-            return sm_algo_pack.cudnn_from_enum(algo);
-        }
-#endif
 #if CUDNN_MAJOR >= 7
         int max_count = 0;
         cudnn_check(cudnnGetConvolutionBackwardDataAlgorithmMaxCount(

dnn/src/cuda/convolution/opr_impl.h

@@ -24,14 +24,6 @@ class ConvolutionForwardImpl: public ConvolutionForward {
                   const PreprocessedFilter* preprocessed_filter,
                   _megdnn_workspace workspace) override;
 
-        std::vector<Algorithm *> get_all_algorithms(const TensorLayout &src,
-                const TensorLayout &filter,
-                const TensorLayout &dst) override;
-        Algorithm* get_algorithm_heuristic(const TensorLayout& src,
-                                           const TensorLayout& filter,
-                                           const TensorLayout& dst,
-                                           size_t workspace_limit_in_bytes,
-                                           bool reproducible) override;
         size_t get_workspace_in_bytes(
                 const TensorLayout& src, const TensorLayout& filter,
                 const TensorLayout& dst,
@@ -60,99 +52,129 @@ class ConvolutionForwardImpl: public ConvolutionForward {
             TensorLayout bias_layout;
             TensorLayout z_layout;
         };
-    private:
-        ConvBiasExtraData conv_bias_extra_data(const TensorLayout&,
-                                               const TensorLayout&,
-                                               const TensorLayout&);
-};
 
-class ConvolutionBackwardDataImpl: public ConvolutionBackwardData {
-    public:
-        using ConvolutionBackwardData::ConvolutionBackwardData;
-        void exec(_megdnn_tensor_in filter,
-                _megdnn_tensor_in diff,
-                _megdnn_tensor_out grad,
-                _megdnn_workspace workspace) override;
-        std::vector<Algorithm *> get_all_algorithms(const TensorLayout &filter,
-                const TensorLayout &diff,
-                const TensorLayout &grad) override;
-        Algorithm* get_algorithm_heuristic(const TensorLayout& filter,
-                                           const TensorLayout& diff,
-                                           const TensorLayout& grad,
+        std::vector<Algorithm*> get_all_algorithms(
+                const TensorLayout& src, const TensorLayout& filter,
+                const TensorLayout& dst) override;
+        Algorithm* get_algorithm_heuristic(const TensorLayout& src,
+                                           const TensorLayout& filter,
+                                           const TensorLayout& dst,
                                            size_t workspace_limit_in_bytes,
                                            bool reproducible) override;
-        Algorithm* get_algorithm_heuristic(
-                const TensorLayout& filter,
-                const CanonizedFilterMeta& filter_meta,
-                const TensorLayout& diff, const TensorLayout& grad,
-                size_t workspace_limit_in_bytes, bool reproducible);
-        size_t get_workspace_in_bytes(const TensorLayout& filter,
-                                      const TensorLayout& diff,
-                                      const TensorLayout& grad) override;
-        const char* get_algorithm_set_name() const override;
-
-        class AlgoBase;
-        class AlgoCUDNN;
-        class AlgoMatmul;
-        class AlgoChanwise;
-        class AlgoChanwiseSmall;
-        class AlgoGroupConvGeneral;
-        class AlgoBFloat16;
-
-        class AlgoPack;
-
-        static const AlgoPack& algo_pack() {
-            return sm_algo_pack;
-        }
 
     private:
-        static AlgoPack sm_algo_pack;
+        ConvBiasExtraData conv_bias_extra_data(const TensorLayout&,
+                                               const TensorLayout&,
+                                               const TensorLayout&);
 };
 
-class ConvolutionBackwardFilterImpl: public ConvolutionBackwardFilter {
-    public:
-        using ConvolutionBackwardFilter::ConvolutionBackwardFilter;
-        void exec(_megdnn_tensor_in src,
-                _megdnn_tensor_in diff,
-                _megdnn_tensor_out grad,
-                _megdnn_workspace workspace) override;
-        std::vector<Algorithm *> get_all_algorithms(const TensorLayout &src,
-                const TensorLayout &diff,
-                const TensorLayout &grad) override;
-        Algorithm* get_algorithm_heuristic(const TensorLayout& src,
-                                           const TensorLayout& diff,
-                                           const TensorLayout& grad,
-                                           size_t workspace_limit_in_bytes,
-                                           bool reproducible) override;
-        Algorithm* get_algorithm_heuristic(const TensorLayout& src,
-                                           const TensorLayout& diff,
-                                           const TensorLayout& gradk,
-                                           const CanonizedFilterMeta& grad_meta,
-                                           size_t workspace_limit_in_bytes,
-                                           bool reproducible);
-        size_t get_workspace_in_bytes(const TensorLayout& src,
-                                      const TensorLayout& diff,
-                                      const TensorLayout& grad) override;
-        const char* get_algorithm_set_name() const override;
-
-        class AlgoBase;
-        class AlgoCUDNN;
-        class AlgoMatmul;
-        class AlgoChanwise;
-        class AlgoGroupConvGeneral;
-        class AlgoBFloat16;
-
-        class AlgoPack;
-
-        static const AlgoPack& algo_pack() {
-            return sm_algo_pack;
-        }
+class ConvolutionBackwardDataImpl : public ConvolutionBackwardData {
+public:
+    using ConvolutionBackwardData::ConvolutionBackwardData;
+    void exec(_megdnn_tensor_in filter, _megdnn_tensor_in diff,
+              _megdnn_tensor_out grad, _megdnn_workspace workspace) override;
+    AlgorithmInfo get_algorithm_info_heuristic(
+            const TensorLayout& filter, const CanonizedFilterMeta& filter_meta,
+            const TensorLayout& diff, const TensorLayout& grad,
+            size_t workspace_limit_in_bytes, bool reproducible) {
+        return get_algorithm_heuristic(filter, filter_meta, diff, grad,
+                                       workspace_limit_in_bytes, reproducible)
+                ->info();
+    }
+    size_t get_workspace_in_bytes(const TensorLayout& filter,
+                                  const TensorLayout& diff,
+                                  const TensorLayout& grad) override;
+    const char* get_algorithm_set_name() const override;
+
+    class AlgoBase;
+    class AlgoCUDNN;
+    class AlgoMatmul;
+    class AlgoChanwise;
+    class AlgoChanwiseSmall;
+    class AlgoGroupConvGeneral;
+    class AlgoBFloat16;
+
+    class AlgoPack;
+
+    static const AlgoPack& algo_pack() { return sm_algo_pack; }
+
+    static AlgoBase* get_algo_from_desc(const AlgorithmDesc& desc);
+
+protected:
+    std::vector<Algorithm*> get_all_algorithms(
+            const TensorLayout& filter, const TensorLayout& diff,
+            const TensorLayout& grad) override;
+    Algorithm* get_algorithm_heuristic(const TensorLayout& filter,
+                                       const TensorLayout& diff,
+                                       const TensorLayout& grad,
+                                       size_t workspace_limit_in_bytes,
+                                       bool reproducible) override;
+private:
+    Algorithm* get_algorithm_heuristic(const TensorLayout& filter,
+                                       const CanonizedFilterMeta& filter_meta,
+                                       const TensorLayout& diff,
+                                       const TensorLayout& grad,
+                                       size_t workspace_limit_in_bytes,
+                                       bool reproducible);
+
+    static AlgoPack sm_algo_pack;
+};
 
-    private:
-        static AlgoPack sm_algo_pack;
+class ConvolutionBackwardFilterImpl : public ConvolutionBackwardFilter {
+public:
+    using ConvolutionBackwardFilter::ConvolutionBackwardFilter;
+    void exec(_megdnn_tensor_in src, _megdnn_tensor_in diff,
+              _megdnn_tensor_out grad, _megdnn_workspace workspace) override;
+    size_t get_workspace_in_bytes(const TensorLayout& src,
+                                  const TensorLayout& diff,
+                                  const TensorLayout& grad) override;
+    AlgorithmInfo get_algorithm_info_heuristic(const TensorLayout& src,
+                                       const TensorLayout& diff,
+                                       const TensorLayout& grad,
+                                       const CanonizedFilterMeta& grad_meta,
+                                       size_t workspace_limit_in_bytes,
+                                       bool reproducible) {
+        return get_algorithm_heuristic(src, diff, grad, grad_meta,
+                                       workspace_limit_in_bytes, reproducible)
+                ->info();
+    }
+
+    const char* get_algorithm_set_name() const override;
+
+    class AlgoBase;
+    class AlgoCUDNN;
+    class AlgoMatmul;
+    class AlgoChanwise;
+    class AlgoGroupConvGeneral;
+    class AlgoBFloat16;
+
+    class AlgoPack;
+
+    static const AlgoPack& algo_pack() { return sm_algo_pack; }
+
+    static AlgoBase* get_algo_from_desc(const AlgorithmDesc& desc);
+
+protected:
+    std::vector<Algorithm*> get_all_algorithms(
+            const TensorLayout& src, const TensorLayout& diff,
+            const TensorLayout& grad) override;
+    Algorithm* get_algorithm_heuristic(const TensorLayout& src,
+                                       const TensorLayout& diff,
+                                       const TensorLayout& grad,
+                                       size_t workspace_limit_in_bytes,
+                                       bool reproducible) override;
+private:
+    Algorithm* get_algorithm_heuristic(const TensorLayout& src,
+                                       const TensorLayout& diff,
+                                       const TensorLayout& grad,
+                                       const CanonizedFilterMeta& grad_meta,
+                                       size_t workspace_limit_in_bytes,
+                                       bool reproducible);
+
+    static AlgoPack sm_algo_pack;
 };
 
-} // namespace cuda
-} // namespace megdnn
+}  // namespace cuda
+}  // namespace megdnn
 
 // vim: syntax=cpp.doxygen

dnn/src/cuda/convolution3d/backward_data/algo.cpp

@@ -39,8 +39,14 @@ Convolution3DBackwardDataImpl::AlgoPack::AlgoPack() {
         all_algos.push_back(&i);
     }
     megdnn_assert(all_algos_data == all_algos.data());
+
+    for (auto&& algo : all_algos) {
+        m_all_algos_map.emplace(algo->info().desc, algo);
+    }
 }
 
+MEGDNN_DEF_GET_ALGO_FROM_DESC(Convolution3DBackwardDataImpl)
+
 Convolution3DBackwardDataImpl::AlgoCUDNN*
 Convolution3DBackwardDataImpl::AlgoPack::cudnn_from_enum(
         cudnnConvolutionBwdDataAlgo_t algo) {
@@ -96,7 +102,7 @@ std::string Convolution3DBackwardDataImpl::AlgoBase::SizeArgs::to_string() const
                 fm.group, fm.ocpg, fm.icpg, fm.spatial[0], fm.spatial[1], fm.spatial[2],
                 diff_layout->to_string().c_str(),
                 grad_layout->to_string().c_str(),
-                fm.padding[0], fm.padding[1], fm.padding[2], 
+                fm.padding[0], fm.padding[1], fm.padding[2],
                 fm.stride[0], fm.stride[1], fm.stride[2],
                 fm.dilation[0], fm.dilation[1] ,fm.dilation[2],
                 !fm.should_flip,

dnn/src/cuda/convolution3d/backward_data/algo.h

@@ -6,13 +6,16 @@
  *
  * 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.
  */
 
 #pragma once
 
-#include "src/cuda/convolution3d/helper.h"
 #include <unordered_map>
+#include "src/cuda/convolution3d/helper.h"
+#include "src/common/algo_base.h"
+#include "src/common/metahelper.h"
 
 namespace megdnn {
 namespace cuda {
@@ -23,170 +26,174 @@ namespace cuda {
  * All the algo impls should try to support non-contiguous batch dim, for group
  * conv execution.
  */
-class Convolution3DBackwardDataImpl::AlgoBase: public Algorithm {
-    protected:
-        ~AlgoBase() = default;
-
-    public:
-        AlgoBase() : Algorithm() { m_handle_type = Handle::HandleType::CUDA; }
-        struct SizeArgs {
-            HandleImpl *handle;
-            CanonizedFilterMeta filter_meta;
-            const TensorLayout *diff_layout, *grad_layout;
-            Convolution3DBackwardDataImpl *opr;
-
-            std::string to_string() const;
-            void init_desc(convolution3d::CUDNNBwdDataDescs &desc) const {
-                desc.set(filter_meta, *diff_layout, *grad_layout, opr->param());
-            }
-            SizeArgs(Convolution3DBackwardDataImpl *opr,
-                    const TensorLayout &filter, const TensorLayout &diff,
-                    const TensorLayout &grad);
-            SizeArgs(Convolution3DBackwardDataImpl *opr,
-                    const CanonizedFilterMeta &filter, const TensorLayout &diff,
-                    const TensorLayout &grad);
-
-            convolution3d::ForwardSizeArgs as_fwd_args() const {
-                return {handle, grad_layout, filter_meta, diff_layout,
-                    opr->param().data_type};
-            }
-        };
-        struct ExecArgs: public SizeArgs {
-            const TensorND *filter_tensor, *diff_tensor, *grad_tensor;
-            Workspace workspace;
-
-            ExecArgs(Convolution3DBackwardDataImpl *opr,
-                    _megdnn_tensor_in filter,
-                    _megdnn_tensor_in diff,
-                    _megdnn_tensor_out grad,
-                    _megdnn_workspace workspace);
-        };
-        virtual bool is_available(const SizeArgs &args) const = 0;
-        virtual size_t get_workspace_in_bytes(const SizeArgs &args) const = 0;
-        virtual void exec(const ExecArgs &args) const = 0;
-
-        bool is_available_wk(const SizeArgs &args, size_t limit) {
-            return is_available(args) && get_workspace_in_bytes(args) <= limit;
-        }
-        bool is_available_reproducible(
-                const SizeArgs& args, bool reproducible = true,
-                size_t limit = std::numeric_limits<size_t>::max()) {
-            return (!reproducible || is_reproducible()) &&
-                   is_available_wk(args, limit);
-        }
-        AlgoBase& check_workspace(
-                const SizeArgs &args, const Workspace &workspace) {
-            auto req = get_workspace_in_bytes(args);
-            megdnn_assert(req <= workspace.size,
-                    "conv bwd data algo %s: "
-                    "required workspace %zu bytes, got %zu",
-                    name(), req, workspace.size);
-            return *this;
+class Convolution3DBackwardDataImpl::AlgoBase : public Algorithm {
+protected:
+    ~AlgoBase() = default;
+
+public:
+    enum class AlgoType : uint32_t {
+        CUDA_GROUP_CONV_GENERAL,
+        CUDA_CUDNN,
+        CUDA_CHANWISE,
+    };
+    using Mapper = std::unordered_map<AlgorithmDesc, AlgoBase*>;
+
+
+    AlgoBase() : Algorithm() { m_handle_type = Handle::HandleType::CUDA; }
+    struct SizeArgs {
+        HandleImpl* handle;
+        CanonizedFilterMeta filter_meta;
+        const TensorLayout *diff_layout, *grad_layout;
+        Convolution3DBackwardDataImpl* opr;
+
+        std::string to_string() const;
+        void init_desc(convolution3d::CUDNNBwdDataDescs& desc) const {
+            desc.set(filter_meta, *diff_layout, *grad_layout, opr->param());
         }
-
-        virtual bool is_cudnn() const {
-            return false;
+        SizeArgs(Convolution3DBackwardDataImpl* opr, const TensorLayout& filter,
+                 const TensorLayout& diff, const TensorLayout& grad);
+        SizeArgs(Convolution3DBackwardDataImpl* opr,
+                 const CanonizedFilterMeta& filter, const TensorLayout& diff,
+                 const TensorLayout& grad);
+
+        convolution3d::ForwardSizeArgs as_fwd_args() const {
+            return {handle, grad_layout, filter_meta, diff_layout,
+                    opr->param().data_type};
         }
+    };
+    struct ExecArgs : public SizeArgs {
+        const TensorND *filter_tensor, *diff_tensor, *grad_tensor;
+        Workspace workspace;
+
+        ExecArgs(Convolution3DBackwardDataImpl* opr, _megdnn_tensor_in filter,
+                 _megdnn_tensor_in diff, _megdnn_tensor_out grad,
+                 _megdnn_workspace workspace);
+    };
+    virtual bool is_available(const SizeArgs& args) const = 0;
+    virtual size_t get_workspace_in_bytes(const SizeArgs& args) const = 0;
+    virtual void exec(const ExecArgs& args) const = 0;
+
+    bool is_available_wk(const SizeArgs& args, size_t limit) {
+        return is_available(args) && get_workspace_in_bytes(args) <= limit;
+    }
+    bool is_available_reproducible(
+            const SizeArgs& args, bool reproducible = true,
+            size_t limit = std::numeric_limits<size_t>::max()) {
+        return (!reproducible || is_reproducible()) &&
+               is_available_wk(args, limit);
+    }
+    AlgoBase& check_workspace(const SizeArgs& args,
+                              const Workspace& workspace) {
+        auto req = get_workspace_in_bytes(args);
+        megdnn_assert(req <= workspace.size,
+                      "conv bwd data algo %s: "
+                      "required workspace %zu bytes, got %zu",
+                      name(), req, workspace.size);
+        return *this;
+    }
+
+    virtual bool is_cudnn() const { return false; }
 };
 
 class Convolution3DBackwardDataImpl::AlgoCUDNN final : public AlgoBase {
-    bool m_is_reproducible;
-    const char *m_name;
     cudnnConvolutionBwdDataAlgo_t m_cudnn_enum;
+    CudnnAlgoPack::Attr m_attr;
 
-    public:
+public:
+    AlgoCUDNN(cudnnConvolutionBwdDataAlgo_t cudnn_enum)
+            : m_cudnn_enum(cudnn_enum) {
+        megdnn_assert(CudnnAlgoPack::conv3d_bwd_data_algos().find(cudnn_enum) !=
+                      CudnnAlgoPack::conv3d_bwd_data_algos().end());
+        m_attr = CudnnAlgoPack::conv3d_bwd_data_algos().at(cudnn_enum);
+    }
 
-        AlgoCUDNN(bool is_reproducible, const char *name,
-                cudnnConvolutionBwdDataAlgo_t cudnn_enum):
-            m_is_reproducible(is_reproducible),
-            m_name(name),
-            m_cudnn_enum(cudnn_enum)
-        {}
+    bool is_available(const SizeArgs& args) const override;
+    size_t get_workspace_in_bytes(const SizeArgs& args) const override;
+    void exec(const ExecArgs& args) const override;
 
-        bool is_available(const SizeArgs &args) const override;
-        size_t get_workspace_in_bytes(const SizeArgs &args) const override;
-        void exec(const ExecArgs &args) const override;
+    bool is_reproducible() const override { return m_attr.is_reproducible; }
 
-        bool is_reproducible() const override {
-            return m_is_reproducible;
-        }
+    const char* name() const override { return m_attr.name.c_str(); }
 
-        const char* name() const override {
-            return m_name;
-        }
+    cudnnConvolutionBwdDataAlgo_t cudnn_enum() const { return m_cudnn_enum; }
 
-        cudnnConvolutionBwdDataAlgo_t cudnn_enum() const {
-            return m_cudnn_enum;
-        }
+    bool is_cudnn() const override { return true; }
 
-        bool is_cudnn() const override {
-            return true;
-        }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_CUDNN)
+
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_cudnn_enum, ret);
+        return ret;
+    }
 };
 
-class Convolution3DBackwardDataImpl::AlgoChanwise final: public AlgoBase {
-    public:
-        bool is_available(const SizeArgs &args) const override;
-        size_t get_workspace_in_bytes(const SizeArgs &args) const override;
-        void exec(const ExecArgs &args) const override;
+class Convolution3DBackwardDataImpl::AlgoChanwise final : public AlgoBase {
+public:
+    bool is_available(const SizeArgs& args) const override;
+    size_t get_workspace_in_bytes(const SizeArgs& args) const override;
+    void exec(const ExecArgs& args) const override;
 
-        const char* name() const override {
-            return "CHANNEL_WISE";
-        }
-        bool is_reproducible() const override {
-            return true;
-        }
+    const char* name() const override { return "CHANNEL_WISE"; }
+    bool is_reproducible() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_CHANWISE)
 };
 
 //! implement group conv by another algo
-class Convolution3DBackwardDataImpl::AlgoGroupConvGeneral final: public AlgoBase {
-    AlgoBase *m_impl;
+class Convolution3DBackwardDataImpl::AlgoGroupConvGeneral final
+        : public AlgoBase {
+    AlgoBase* m_impl;
     std::string m_name;
 
-    public:
-        AlgoGroupConvGeneral(AlgoBase *impl);
+public:
+    AlgoGroupConvGeneral(AlgoBase* impl);
 
-        bool is_available(const SizeArgs &args) const override;
-        size_t get_workspace_in_bytes(const SizeArgs &args) const override;
-        void exec(const ExecArgs &args) const override;
+    bool is_available(const SizeArgs& args) const override;
+    size_t get_workspace_in_bytes(const SizeArgs& args) const override;
+    void exec(const ExecArgs& args) const override;
 
-        const char* name() const override {
-            return m_name.c_str();
-        }
+    const char* name() const override { return m_name.c_str(); }
 
-        bool is_reproducible() const override {
-            return m_impl->is_reproducible();
-        }
+    bool is_reproducible() const override { return m_impl->is_reproducible(); }
+
+    static void modify_size_args(SizeArgs& args, TensorLayout& diff_pg,
+                                 TensorLayout& grad_pg);
 
-        static void modify_size_args(SizeArgs &args,
-                TensorLayout &diff_pg, TensorLayout &grad_pg);
+    MEGDNN_DECL_ALGO_TYPE(CUDA_GROUP_CONV_GENERAL)
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_impl, ret);
+        return ret;
+    }
 };
 
-class Convolution3DBackwardDataImpl::AlgoPack {
+
+class Convolution3DBackwardDataImpl::AlgoPack : NonCopyableObj {
     // defined in cudnn.cpp
     void fill_cudnn_algos();
 
-    AlgoPack(const AlgoPack&) = delete;
-    AlgoPack& operator = (const AlgoPack &) = delete;
+    AlgoBase::Mapper m_all_algos_map;
 
-    public:
-        AlgoPack();
+public:
+    AlgoPack();
 
-        std::vector<AlgoCUDNN> cudnn;
-        AlgoChanwise chanwise;
-        std::vector<AlgoGroupConvGeneral> gconv;
-        std::unordered_map<AlgoBase*, AlgoGroupConvGeneral*> algo2gconv;
+    std::vector<AlgoCUDNN> cudnn;
+    AlgoChanwise chanwise;
+    std::vector<AlgoGroupConvGeneral> gconv;
+    std::unordered_map<AlgoBase*, AlgoGroupConvGeneral*> algo2gconv;
 
-        std::vector<AlgoBase*>
+    std::vector<AlgoBase*>
             //! all algorithms
             all_algos,
             //! non-cudnn algos, used for heuristic if cudnn is not supported
             non_cudnn_algos;
 
-        AlgoCUDNN* cudnn_from_enum(cudnnConvolutionBwdDataAlgo_t algo);
+    AlgoCUDNN* cudnn_from_enum(cudnnConvolutionBwdDataAlgo_t algo);
+
+    const AlgoBase::Mapper& all_algos_map() const { return m_all_algos_map; }
 };
 
-} // namespace cuda
-} // namespace megdnn
+}  // namespace cuda
+}  // namespace megdnn
 
 // vim: syntax=cpp.doxygen

dnn/src/cuda/convolution3d/backward_data/cudnn.cpp

@@ -80,27 +80,9 @@ void Convolution3DBackwardDataImpl::AlgoCUDNN::exec(
 }
 
 void Convolution3DBackwardDataImpl::AlgoPack::fill_cudnn_algos() {
-#define V1(v) #v
-#define V(v) V1(v)
-
-#define DEF_ALGO(NAME, REPROD) \
-    cudnn.push_back({ \
-                REPROD, #NAME \
-                    "v" V(CUDNN_MAJOR) "." V(CUDNN_MINOR) \
-                    "." V(CUDNN_PATCHLEVEL), \
-                NAME})
-
-DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_0, false);
-DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_1, true);
-DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT_TILING, true);
-#if !(CUDNN_MAJOR >= 6 || CUDNN_MINOR >= 1)
-#pragma message "not latest cudnn"
-#endif
-
-#undef DEF_ALGO
-
-#undef V
-#undef V1
+    for (auto&& algo : CudnnAlgoPack::conv3d_bwd_data_algos()) {
+        cudnn.push_back(algo.first);
+    }
 }
 
 // vim: syntax=cpp.doxygen

dnn/src/cuda/convolution3d/backward_filter/algo.cpp

@@ -17,7 +17,7 @@ using namespace cuda;
 
 Convolution3DBackwardFilterImpl::AlgoPack::AlgoPack() {
     non_cudnn_algos.push_back(&chanwise);
-    non_cudnn_algos.push_back(&inplace_matmul); 
+    non_cudnn_algos.push_back(&inplace_matmul);
     all_algos.push_back(&chanwise); // prefer chanwise
 
     fill_cudnn_algos();
@@ -41,8 +41,14 @@ Convolution3DBackwardFilterImpl::AlgoPack::AlgoPack() {
     }
     megdnn_assert(all_algos_data == all_algos.data());
     non_cudnn_algos.push_back(all_algos.rbegin()[0]); //group inplace_matmul
+
+    for (auto&& algo : all_algos) {
+        m_all_algos_map.emplace(algo->info().desc, algo);
+    }
 }
 
+MEGDNN_DEF_GET_ALGO_FROM_DESC(Convolution3DBackwardFilterImpl)
+
 Convolution3DBackwardFilterImpl::AlgoCUDNN*
 Convolution3DBackwardFilterImpl::AlgoPack::cudnn_from_enum(
         cudnnConvolutionBwdFilterAlgo_t algo) {
@@ -99,9 +105,9 @@ Convolution3DBackwardFilterImpl::AlgoBase::SizeArgs::to_string() const {
                 "pad=%ux%ux%u, stride=%ux%ux%u, dilate=%ux%ux%u, xcorr=%d, dtype=%s,%s",
                 src_layout->to_string().c_str(),
                 diff_layout->to_string().c_str(),
-                fm.group, fm.ocpg, fm.icpg, 
+                fm.group, fm.ocpg, fm.icpg,
                 fm.spatial[0], fm.spatial[1], fm.spatial[2],
-                fm.padding[0], fm.padding[1], fm.padding[2], 
+                fm.padding[0], fm.padding[1], fm.padding[2],
                 fm.stride[0], fm.stride[1], fm.stride[2],
                 fm.dilation[0], fm.dilation[1], fm.dilation[2],
                 !fm.should_flip,

dnn/src/cuda/convolution3d/backward_filter/algo.h

@@ -6,198 +6,198 @@
  *
  * 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.
  */
 
 #pragma once
 
-#include "src/cuda/convolution3d/helper.h"
 #include <unordered_map>
+#include "src/cuda/convolution3d/helper.h"
+#include "src/common/algo_base.h"
+#include "src/common/metahelper.h"
 
 namespace megdnn {
 namespace cuda {
 
-class Convolution3DBackwardFilterImpl::AlgoBase: public Algorithm {
-    protected:
-        ~AlgoBase() = default;
-
-    public:
-        AlgoBase() : Algorithm() { m_handle_type = Handle::HandleType::CUDA; }
-        struct SizeArgs {
-            HandleImpl *handle;
-            const TensorLayout *src_layout, *diff_layout;
-            CanonizedFilterMeta grad_filter_meta;
-            Convolution3DBackwardFilterImpl *opr;
-
-            std::string to_string() const;
-            void init_desc(convolution3d::CUDNNBwdFilterDescs &desc) const {
-                desc.set(*src_layout, *diff_layout, grad_filter_meta,
-                        opr->param());
-            }
-            SizeArgs(Convolution3DBackwardFilterImpl *opr,
-                    const TensorLayout &src, const TensorLayout &diff,
-                    const TensorLayout &grad);
-            SizeArgs(Convolution3DBackwardFilterImpl *opr,
-                    const TensorLayout &src, const TensorLayout &diff,
-                    const CanonizedFilterMeta &grad);
-
-            convolution3d::ForwardSizeArgs as_fwd_args() const {
-                return {handle, src_layout, grad_filter_meta, diff_layout,
-                    opr->param().data_type};
-            }
-        };
-        struct ExecArgs: public SizeArgs {
-            const TensorND *src_tensor, *diff_tensor, *grad_tensor;
-            Workspace workspace;
-
-            ExecArgs(Convolution3DBackwardFilterImpl *opr,
-                    _megdnn_tensor_in src,
-                    _megdnn_tensor_in diff,
-                    _megdnn_tensor_out grad,
-                    _megdnn_workspace workspace);
-        };
-        virtual bool is_available(const SizeArgs &args) const = 0;
-        virtual size_t get_workspace_in_bytes(const SizeArgs &args) const = 0;
-        virtual void exec(const ExecArgs &args) const = 0;
-
-        bool is_available_wk(const SizeArgs &args, size_t limit) {
-            return is_available(args) && get_workspace_in_bytes(args) <= limit;
-        }
-        bool is_available_reproducible(
-                const SizeArgs& args, bool reproducible = true,
-                size_t limit = std::numeric_limits<size_t>::max()) {
-            return (!reproducible || is_reproducible()) &&
-                   is_available_wk(args, limit);
-        }
-        AlgoBase& check_workspace(const SizeArgs& args,
-                                  const Workspace& workspace) {
-            auto req = get_workspace_in_bytes(args);
-            megdnn_assert(req <= workspace.size,
-                    "conv bwd filter algo %s: "
-                    "required workspace %zu bytes, got %zu",
-                    name(), req, workspace.size);
-            return *this;
+class Convolution3DBackwardFilterImpl::AlgoBase : public Algorithm {
+protected:
+    ~AlgoBase() = default;
+
+public:
+    AlgoBase() : Algorithm() { m_handle_type = Handle::HandleType::CUDA; }
+    enum class AlgoType : uint32_t {
+        CUDA_GROUP_CONV_GENERAL,
+        CUDA_CUDNN,
+        CUDA_INPLACE_MATMUL,
+        CUDA_CHANWISE,
+    };
+    using Mapper = std::unordered_map<AlgorithmDesc, AlgoBase*>;
+
+    struct SizeArgs {
+        HandleImpl* handle;
+        const TensorLayout *src_layout, *diff_layout;
+        CanonizedFilterMeta grad_filter_meta;
+        Convolution3DBackwardFilterImpl* opr;
+
+        std::string to_string() const;
+        void init_desc(convolution3d::CUDNNBwdFilterDescs& desc) const {
+            desc.set(*src_layout, *diff_layout, grad_filter_meta, opr->param());
         }
+        SizeArgs(Convolution3DBackwardFilterImpl* opr, const TensorLayout& src,
+                 const TensorLayout& diff, const TensorLayout& grad);
+        SizeArgs(Convolution3DBackwardFilterImpl* opr, const TensorLayout& src,
+                 const TensorLayout& diff, const CanonizedFilterMeta& grad);
 
-        virtual bool is_cudnn() const {
-            return false;
+        convolution3d::ForwardSizeArgs as_fwd_args() const {
+            return {handle, src_layout, grad_filter_meta, diff_layout,
+                    opr->param().data_type};
         }
+    };
+    struct ExecArgs : public SizeArgs {
+        const TensorND *src_tensor, *diff_tensor, *grad_tensor;
+        Workspace workspace;
+
+        ExecArgs(Convolution3DBackwardFilterImpl* opr, _megdnn_tensor_in src,
+                 _megdnn_tensor_in diff, _megdnn_tensor_out grad,
+                 _megdnn_workspace workspace);
+    };
+    virtual bool is_available(const SizeArgs& args) const = 0;
+    virtual size_t get_workspace_in_bytes(const SizeArgs& args) const = 0;
+    virtual void exec(const ExecArgs& args) const = 0;
+
+    bool is_available_wk(const SizeArgs& args, size_t limit) {
+        return is_available(args) && get_workspace_in_bytes(args) <= limit;
+    }
+    bool is_available_reproducible(
+            const SizeArgs& args, bool reproducible = true,
+            size_t limit = std::numeric_limits<size_t>::max()) {
+        return (!reproducible || is_reproducible()) &&
+               is_available_wk(args, limit);
+    }
+    AlgoBase& check_workspace(const SizeArgs& args,
+                              const Workspace& workspace) {
+        auto req = get_workspace_in_bytes(args);
+        megdnn_assert(req <= workspace.size,
+                      "conv bwd filter algo %s: "
+                      "required workspace %zu bytes, got %zu",
+                      name(), req, workspace.size);
+        return *this;
+    }
+
+    virtual bool is_cudnn() const { return false; }
 };
 
 class Convolution3DBackwardFilterImpl::AlgoCUDNN final : public AlgoBase {
-    bool m_is_reproducible;
-    const char *m_name;
     cudnnConvolutionBwdFilterAlgo_t m_cudnn_enum;
+    CudnnAlgoPack::Attr m_attr;
 
-    public:
+public:
+    AlgoCUDNN(cudnnConvolutionBwdFilterAlgo_t cudnn_enum)
+            : m_cudnn_enum(cudnn_enum) {
+        megdnn_assert(CudnnAlgoPack::conv3d_bwd_flt_algos().find(cudnn_enum) !=
+                      CudnnAlgoPack::conv3d_bwd_flt_algos().end());
+        m_attr = CudnnAlgoPack::conv3d_bwd_flt_algos().at(cudnn_enum);
+    }
 
-        AlgoCUDNN(bool is_reproducible, const char *name,
-                cudnnConvolutionBwdFilterAlgo_t cudnn_enum):
-            m_is_reproducible(is_reproducible),
-            m_name(name),
-            m_cudnn_enum(cudnn_enum)
-        {}
+    bool is_available(const SizeArgs& args) const override;
+    size_t get_workspace_in_bytes(const SizeArgs& args) const override;
+    void exec(const ExecArgs& args) const override;
 
-        bool is_available(const SizeArgs &args) const override;
-        size_t get_workspace_in_bytes(const SizeArgs &args) const override;
-        void exec(const ExecArgs &args) const override;
+    bool is_reproducible() const override { return m_attr.is_reproducible; }
 
-        bool is_reproducible() const override {
-            return m_is_reproducible;
-        }
+    const char* name() const override { return m_attr.name.c_str(); }
 
-        const char* name() const override {
-            return m_name;
-        }
+    cudnnConvolutionBwdFilterAlgo_t cudnn_enum() const { return m_cudnn_enum; }
 
-        cudnnConvolutionBwdFilterAlgo_t cudnn_enum() const {
-            return m_cudnn_enum;
-        }
+    bool is_cudnn() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_CUDNN)
 
-        bool is_cudnn() const override {
-            return true;
-        }
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_cudnn_enum, ret);
+        return ret;
+    }
 };
 
+class Convolution3DBackwardFilterImpl::AlgoInplaceMatmul final
+        : public AlgoBase {
+public:
+    bool is_available(const SizeArgs& args) const override;
+    size_t get_workspace_in_bytes(const SizeArgs& args) const override;
+    void exec(const ExecArgs& args) const override;
 
-class Convolution3DBackwardFilterImpl::AlgoInplaceMatmul final: public AlgoBase {
-    public:
-        bool is_available(const SizeArgs &args) const override;
-        size_t get_workspace_in_bytes(const SizeArgs &args) const override;
-        void exec(const ExecArgs &args) const override;
-
-        const char* name() const override {
-            return "INPLACE_MATMUL";
-        }
-        bool is_reproducible() const override {
-            return false;
-        }
+    const char* name() const override { return "INPLACE_MATMUL"; }
+    bool is_reproducible() const override { return false; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_INPLACE_MATMUL)
 };
 
-class Convolution3DBackwardFilterImpl::AlgoChanwise final: public AlgoBase {
-    public:
-        bool is_available(const SizeArgs &args) const override;
-        size_t get_workspace_in_bytes(const SizeArgs &args) const override;
-        void exec(const ExecArgs &args) const override;
+class Convolution3DBackwardFilterImpl::AlgoChanwise final : public AlgoBase {
+public:
+    bool is_available(const SizeArgs& args) const override;
+    size_t get_workspace_in_bytes(const SizeArgs& args) const override;
+    void exec(const ExecArgs& args) const override;
 
-        const char* name() const override {
-            return "CHANNEL_WISE";
-        }
-        bool is_reproducible() const override {
-            return true;
-        }
+    const char* name() const override { return "CHANNEL_WISE"; }
+    bool is_reproducible() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_CHANWISE)
 };
 
 //! implement group conv by another algo
-class Convolution3DBackwardFilterImpl::AlgoGroupConvGeneral final: public AlgoBase {
-    AlgoBase *m_impl;
+class Convolution3DBackwardFilterImpl::AlgoGroupConvGeneral final
+        : public AlgoBase {
+    AlgoBase* m_impl;
     std::string m_name;
 
-    public:
-        AlgoGroupConvGeneral(AlgoBase *impl);
+public:
+    AlgoGroupConvGeneral(AlgoBase* impl);
 
-        bool is_available(const SizeArgs &args) const override;
-        size_t get_workspace_in_bytes(const SizeArgs &args) const override;
-        void exec(const ExecArgs &args) const override;
+    bool is_available(const SizeArgs& args) const override;
+    size_t get_workspace_in_bytes(const SizeArgs& args) const override;
+    void exec(const ExecArgs& args) const override;
 
-        const char* name() const override {
-            return m_name.c_str();
-        }
+    const char* name() const override { return m_name.c_str(); }
 
-        bool is_reproducible() const override {
-            return m_impl->is_reproducible();
-        }
+    bool is_reproducible() const override { return m_impl->is_reproducible(); }
 
-        static void modify_size_args(SizeArgs &args,
-                TensorLayout &src_pg, TensorLayout &diff_pg);
+    static void modify_size_args(SizeArgs& args, TensorLayout& src_pg,
+                                 TensorLayout& diff_pg);
+
+    MEGDNN_DECL_ALGO_TYPE(CUDA_GROUP_CONV_GENERAL)
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_impl, ret);
+        return ret;
+    }
 };
 
-class Convolution3DBackwardFilterImpl::AlgoPack {
+class Convolution3DBackwardFilterImpl::AlgoPack : NonCopyableObj {
     // defined in cudnn.cpp
     void fill_cudnn_algos();
 
-    AlgoPack(const AlgoPack&) = delete;
-    AlgoPack& operator = (const AlgoPack &) = delete;
+    AlgoBase::Mapper m_all_algos_map;
 
-    public:
-        AlgoPack();
+public:
+    AlgoPack();
 
-        std::vector<AlgoCUDNN> cudnn;
-        AlgoInplaceMatmul inplace_matmul;
-        AlgoChanwise chanwise;
-        std::vector<AlgoGroupConvGeneral> gconv;
-        std::unordered_map<AlgoBase*, AlgoGroupConvGeneral*> algo2gconv;
+    std::vector<AlgoCUDNN> cudnn;
+    AlgoInplaceMatmul inplace_matmul;
+    AlgoChanwise chanwise;
+    std::vector<AlgoGroupConvGeneral> gconv;
+    std::unordered_map<AlgoBase*, AlgoGroupConvGeneral*> algo2gconv;
 
-        std::vector<AlgoBase*>
+    std::vector<AlgoBase*>
             //! all algorithms
             all_algos,
             //! non-cudnn algos, used for heuristic if cudnn is not supported
             non_cudnn_algos;
 
-        AlgoCUDNN* cudnn_from_enum(cudnnConvolutionBwdFilterAlgo_t algo);
+    AlgoCUDNN* cudnn_from_enum(cudnnConvolutionBwdFilterAlgo_t algo);
+
+    const AlgoBase::Mapper& all_algos_map() const { return m_all_algos_map; }
 };
 
-} // namespace cuda
-} // namespace megdnn
+}  // namespace cuda
+}  // namespace megdnn
 
 // vim: syntax=cpp.doxygen

dnn/src/cuda/convolution3d/backward_filter/cudnn.cpp

@@ -66,29 +66,9 @@ void Convolution3DBackwardFilterImpl::AlgoCUDNN::exec(
 }
 
 void Convolution3DBackwardFilterImpl::AlgoPack::fill_cudnn_algos() {
-#define V1(v) #v
-#define V(v) V1(v)
-
-#define DEF_ALGO(NAME, REPROD)                                          \
-    cudnn.push_back({REPROD,                                            \
-                     #NAME "v" V(CUDNN_MAJOR) "." V(CUDNN_MINOR) "." V( \
-                             CUDNN_PATCHLEVEL),                         \
-                     NAME})
-
-    DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0, false);
-#pragma message \
-        "fp16 dilated conv with odd size filter, only algo_1 works, need focus on doc"
-    DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1, true);
-    DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_3, false);
-
-#if !(CUDNN_MAJOR >= 6 || CUDNN_MINOR >= 1)
-#pragma message "not latest cudnn"
-#endif
-
-#undef DEF_ALGO
-
-#undef V
-#undef V1
+    for (auto&& algo : CudnnAlgoPack::conv3d_bwd_flt_algos()) {
+        cudnn.push_back(algo.first);
+    }
 }
 
 // vim: syntax=cpp.doxygen

dnn/src/cuda/convolution3d/forward/algo.cpp

@@ -21,13 +21,13 @@ Convolution3DForwardImpl::AlgoPack::AlgoPack() {
     non_cudnn_algos.push_back(&a1x1x1);
 
     all_algos.push_back(&chanwise);
-    
+
     fill_cudnn_algos();
     for (auto &&i: cudnn) {
-       all_algos.push_back(&i); 
+       all_algos.push_back(&i);
     }
     all_algos.push_back(&inplace_matmul);
-    all_algos.push_back(&a1x1x1);    
+    all_algos.push_back(&a1x1x1);
     all_algos.reserve(all_algos.size() * 2);
 
     // add gconv algos by AlgoGroupConvGeneral
@@ -42,10 +42,16 @@ Convolution3DForwardImpl::AlgoPack::AlgoPack() {
         all_algos.push_back(&i);
     }
     megdnn_assert(all_algos_data == all_algos.data());
-    non_cudnn_algos.push_back(all_algos.rbegin()[1]); // group inplace_matmul 
+    non_cudnn_algos.push_back(all_algos.rbegin()[1]); // group inplace_matmul
     non_cudnn_algos.push_back(all_algos.rbegin()[0]); // group 1x1x1
+
+    for (auto&& algo : all_algos) {
+        m_all_algos_map.emplace(algo->info().desc, algo);
+    }
 }
 
+MEGDNN_DEF_GET_ALGO_FROM_DESC(Convolution3DForwardImpl)
+
 Convolution3DForwardImpl::AlgoCUDNN*
 Convolution3DForwardImpl::AlgoPack::cudnn_from_enum(
         cudnnConvolutionFwdAlgo_t algo) {
@@ -99,7 +105,7 @@ std::string Convolution3DForwardImpl::AlgoBase::SizeArgs::to_string() const {
                 "src=%s, filter=%u{%u,%u,%u,%u,%u}, dst=%s, "
                 "pad=%ux%ux%u, stride=%ux%ux%u, dilate=%ux%ux%u, xcorr=%d, dtype=%s,%s",
                 src_layout->to_string().c_str(),
-                fm.group, fm.ocpg, fm.icpg, 
+                fm.group, fm.ocpg, fm.icpg,
                 fm.spatial[0], fm.spatial[1], fm.spatial[2],
                 dst_layout->to_string().c_str(),
                 fm.padding[0], fm.padding[1], fm.padding[2],

dnn/src/cuda/convolution3d/forward/algo.h

@@ -6,17 +6,20 @@
  *
  * 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.
  */
 
 #pragma once
 
 #include "megdnn/oprs.h"
 
+#include "src/common/utils.h"
 #include "src/cuda/convolution3d/helper.h"
-#include "src/cuda/handle.h"
 #include "src/cuda/convolution3d/opr_impl.h"
-#include "src/common/utils.h"
+#include "src/cuda/handle.h"
+#include "src/common/algo_base.h"
+#include "src/common/metahelper.h"
 
 #include <unordered_map>
 
@@ -29,195 +32,189 @@ namespace cuda {
  * All the algo impls should try to support non-contiguous batch dim, for group
  * conv execution.
  */
-class Convolution3DForwardImpl::AlgoBase: public Algorithm {
-    protected:
-        ~AlgoBase() = default;
-
-    public:
-        AlgoBase() : Algorithm() { m_handle_type = Handle::HandleType::CUDA; }
-        struct SizeArgs: public convolution3d::ForwardSizeArgs {
-            Convolution3DForwardImpl *opr;
-
-            std::string to_string() const;
-            void init_desc(convolution3d::CUDNNForwardDescs &desc) const {
-                desc.set(*src_layout, filter_meta, *dst_layout, opr->param());
-            }
-            SizeArgs(Convolution3DForwardImpl *opr,
-                    const TensorLayout &src,
-                    const TensorLayout &filter,
-                    const TensorLayout &dst);
-            SizeArgs(Convolution3DForwardImpl *opr,
-                    const TensorLayout &src,
-                    const CanonizedFilterMeta &filter,
-                    const TensorLayout &dst);
-        };
-        struct ExecArgs : public SizeArgs {
-            const TensorND *src_tensor, *filter_tensor, *dst_tensor;
-            Workspace workspace;
-
-            ExecArgs(Convolution3DForwardImpl *opr,
-                    _megdnn_tensor_in src,
-                    _megdnn_tensor_in filter,
-                    _megdnn_tensor_out dst,
-                    _megdnn_workspace workspace);
-        };
-        virtual bool is_available(const SizeArgs &args) const = 0;
-        virtual size_t get_workspace_in_bytes(const SizeArgs &args) const = 0;
-        virtual void exec(const ExecArgs &args) const = 0;
-
-        bool is_available_wk(const SizeArgs &args, size_t limit) {
-            return is_available(args) && get_workspace_in_bytes(args) <= limit;
-        }
-        bool is_available_reproducible(
-                const SizeArgs& args, bool reproducible = true,
-                size_t limit = std::numeric_limits<size_t>::max()) {
-            return (!reproducible || is_reproducible()) &&
-                   is_available_wk(args, limit);
-        }
-        AlgoBase& check_workspace(const SizeArgs& args,
-                                  const Workspace& workspace) {
-            auto req = get_workspace_in_bytes(args);
-            megdnn_assert(req <= workspace.size,
-                    "conv3d fwd algo %s: required workspace %zu bytes, got %zu",
-                    name(), req, workspace.size);
-            return *this;
-        }
-
-        virtual bool is_cudnn() const {
-            return false;
-        }
+class Convolution3DForwardImpl::AlgoBase : public Algorithm {
+protected:
+    ~AlgoBase() = default;
+
+public:
+    enum class AlgoType : uint32_t {
+        CUDA_1X1X1,
+        CUDA_GROUP_CONV_GENERAL,
+        CUDA_CUDNN,
+        CUDA_INPLACE_MATMUL,
+        CUDA_CHANWISE,
+    };
+    using Mapper = std::unordered_map<AlgorithmDesc, AlgoBase*>;
+
+    AlgoBase() : Algorithm() { m_handle_type = Handle::HandleType::CUDA; }
+    struct SizeArgs : public convolution3d::ForwardSizeArgs {
+        Convolution3DForwardImpl* opr;
+
+        std::string to_string() const;
+        void init_desc(convolution3d::CUDNNForwardDescs& desc) const {
+            desc.set(*src_layout, filter_meta, *dst_layout, opr->param());
+        }
+        SizeArgs(Convolution3DForwardImpl* opr, const TensorLayout& src,
+                 const TensorLayout& filter, const TensorLayout& dst);
+        SizeArgs(Convolution3DForwardImpl* opr, const TensorLayout& src,
+                 const CanonizedFilterMeta& filter, const TensorLayout& dst);
+    };
+    struct ExecArgs : public SizeArgs {
+        const TensorND *src_tensor, *filter_tensor, *dst_tensor;
+        Workspace workspace;
+
+        ExecArgs(Convolution3DForwardImpl* opr, _megdnn_tensor_in src,
+                 _megdnn_tensor_in filter, _megdnn_tensor_out dst,
+                 _megdnn_workspace workspace);
+    };
+    virtual bool is_available(const SizeArgs& args) const = 0;
+    virtual size_t get_workspace_in_bytes(const SizeArgs& args) const = 0;
+    virtual void exec(const ExecArgs& args) const = 0;
+
+    bool is_available_wk(const SizeArgs& args, size_t limit) {
+        return is_available(args) && get_workspace_in_bytes(args) <= limit;
+    }
+    bool is_available_reproducible(
+            const SizeArgs& args, bool reproducible = true,
+            size_t limit = std::numeric_limits<size_t>::max()) {
+        return (!reproducible || is_reproducible()) &&
+               is_available_wk(args, limit);
+    }
+    AlgoBase& check_workspace(const SizeArgs& args,
+                              const Workspace& workspace) {
+        auto req = get_workspace_in_bytes(args);
+        megdnn_assert(
+                req <= workspace.size,
+                "conv3d fwd algo %s: required workspace %zu bytes, got %zu",
+                name(), req, workspace.size);
+        return *this;
+    }
+
+    virtual bool is_cudnn() const { return false; }
 };
-class Convolution3DForwardImpl::Algo1x1x1 final: public AlgoBase {
-    static void extract_matmul_layouts(const SizeArgs &args,
-            TensorLayout &A, TensorLayout &B, TensorLayout &C);
-    public:
-        bool is_available(const SizeArgs &args) const override;
-        size_t get_workspace_in_bytes(const SizeArgs &args) const override;
-        void exec(const ExecArgs &args) const override;
-
-        const char* name() const override {
-            return "1x1x1";
-        }
-        bool is_reproducible() const override {
-            return true;
-        }
+class Convolution3DForwardImpl::Algo1x1x1 final : public AlgoBase {
+    static void extract_matmul_layouts(const SizeArgs& args, TensorLayout& A,
+                                       TensorLayout& B, TensorLayout& C);
+
+public:
+    bool is_available(const SizeArgs& args) const override;
+    size_t get_workspace_in_bytes(const SizeArgs& args) const override;
+    void exec(const ExecArgs& args) const override;
+
+    const char* name() const override { return "1x1x1"; }
+    bool is_reproducible() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_1X1X1)
 };
 
 //! implement group conv by another algo
-class Convolution3DForwardImpl::AlgoGroupConvGeneral final: public AlgoBase {
-    AlgoBase *m_impl;
+class Convolution3DForwardImpl::AlgoGroupConvGeneral final : public AlgoBase {
+    AlgoBase* m_impl;
     std::string m_name;
 
-    public:
-        AlgoGroupConvGeneral(AlgoBase *impl);
+public:
+    AlgoGroupConvGeneral(AlgoBase* impl);
 
-        bool is_available(const SizeArgs &args) const override;
-        size_t get_workspace_in_bytes(const SizeArgs &args) const override;
-        void exec(const ExecArgs &args) const override;
+    bool is_available(const SizeArgs& args) const override;
+    size_t get_workspace_in_bytes(const SizeArgs& args) const override;
+    void exec(const ExecArgs& args) const override;
 
-        const char* name() const override {
-            return m_name.c_str();
-        }
+    const char* name() const override { return m_name.c_str(); }
 
-        bool is_reproducible() const override {
-            return m_impl->is_reproducible();
-        }
+    bool is_reproducible() const override { return m_impl->is_reproducible(); }
 
-        static void modify_size_args(SizeArgs &args,
-                TensorLayout &src_pg, TensorLayout &dst_pg);
+    static void modify_size_args(SizeArgs& args, TensorLayout& src_pg,
+                                 TensorLayout& dst_pg);
+    MEGDNN_DECL_ALGO_TYPE(CUDA_GROUP_CONV_GENERAL)
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_impl, ret);
+        return ret;
+    }
 };
 
 class Convolution3DForwardImpl::AlgoCUDNN final : public AlgoBase {
-    bool m_is_reproducible;
-    const char *m_name;
     cudnnConvolutionFwdAlgo_t m_cudnn_enum;
+    CudnnAlgoPack::Attr m_attr;
 
-    public:
+public:
+    AlgoCUDNN(cudnnConvolutionFwdAlgo_t cudnn_enum) : m_cudnn_enum(cudnn_enum) {
+        megdnn_assert(CudnnAlgoPack::conv3d_fwd_algos().find(cudnn_enum) !=
+                      CudnnAlgoPack::conv3d_fwd_algos().end());
+        m_attr = CudnnAlgoPack::conv3d_fwd_algos().at(cudnn_enum);
+    }
 
-        AlgoCUDNN(bool is_reproducible, const char *name,
-                cudnnConvolutionFwdAlgo_t cudnn_enum):
-            m_is_reproducible(is_reproducible),
-            m_name(name),
-            m_cudnn_enum(cudnn_enum)
-        {}
+    bool is_available(const SizeArgs& args) const override;
+    size_t get_workspace_in_bytes(const SizeArgs& args) const override;
+    void exec(const ExecArgs& args) const override;
 
-        bool is_available(const SizeArgs &args) const override;
-        size_t get_workspace_in_bytes(const SizeArgs &args) const override;
-        void exec(const ExecArgs &args) const override;
+    bool is_reproducible() const override { return m_attr.is_reproducible; }
 
-        bool is_reproducible() const override {
-            return m_is_reproducible;
-        }
+    const char* name() const override { return m_attr.name.c_str(); }
 
-        const char* name() const override {
-            return m_name;
-        }
+    cudnnConvolutionFwdAlgo_t cudnn_enum() const { return m_cudnn_enum; }
 
-        cudnnConvolutionFwdAlgo_t cudnn_enum() const {
-            return m_cudnn_enum;
-        }
+    bool is_cudnn() const override { return true; }
 
-        bool is_cudnn() const override {
-            return true;
-        }
-};
+    MEGDNN_DECL_ALGO_TYPE(CUDA_CUDNN)
 
-class Convolution3DForwardImpl::AlgoInplaceMatmul final: public AlgoBase {
-    public:
-        bool is_available(const SizeArgs &args) const override;
-        size_t get_workspace_in_bytes(const SizeArgs &args) const override;
-        void exec(const ExecArgs &args) const override;
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_cudnn_enum, ret);
+        return ret;
+    }
 
-        const char* name() const override {
-            return "INPLACE_MATMUL";
-        }
-        bool is_reproducible() const override {
-            return true;
-        }
 };
 
+class Convolution3DForwardImpl::AlgoInplaceMatmul final : public AlgoBase {
+public:
+    bool is_available(const SizeArgs& args) const override;
+    size_t get_workspace_in_bytes(const SizeArgs& args) const override;
+    void exec(const ExecArgs& args) const override;
 
-class Convolution3DForwardImpl::AlgoChanwise final: public AlgoBase {
-    public:
-        bool is_available(const SizeArgs &args) const override;
-        size_t get_workspace_in_bytes(const SizeArgs &args) const override;
-        void exec(const ExecArgs &args) const override;
+    const char* name() const override { return "INPLACE_MATMUL"; }
+    bool is_reproducible() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_INPLACE_MATMUL)
+};
 
-        const char* name() const override {
-            return "CHANNEL_WISE";
-        }
-        bool is_reproducible() const override {
-            return true;
-        }
+class Convolution3DForwardImpl::AlgoChanwise final : public AlgoBase {
+public:
+    bool is_available(const SizeArgs& args) const override;
+    size_t get_workspace_in_bytes(const SizeArgs& args) const override;
+    void exec(const ExecArgs& args) const override;
+
+    const char* name() const override { return "CHANNEL_WISE"; }
+    bool is_reproducible() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_CHANWISE)
 };
 
-class Convolution3DForwardImpl::AlgoPack {
+class Convolution3DForwardImpl::AlgoPack : NonCopyableObj {
     // defined in cudnn.cpp
     void fill_cudnn_algos();
 
-    AlgoPack(const AlgoPack&) = delete;
-    AlgoPack& operator = (const AlgoPack &) = delete;
+    AlgoBase::Mapper m_all_algos_map;
 
-    public:
-        AlgoPack();
+public:
+    AlgoPack();
 
-        std::vector<AlgoCUDNN> cudnn;
-        Algo1x1x1 a1x1x1;
-        AlgoInplaceMatmul inplace_matmul;
-        AlgoChanwise chanwise;
-        std::vector<AlgoGroupConvGeneral> gconv;
-        std::unordered_map<AlgoBase*, AlgoGroupConvGeneral*> algo2gconv;
+    std::vector<AlgoCUDNN> cudnn;
+    Algo1x1x1 a1x1x1;
+    AlgoInplaceMatmul inplace_matmul;
+    AlgoChanwise chanwise;
+    std::vector<AlgoGroupConvGeneral> gconv;
+    std::unordered_map<AlgoBase*, AlgoGroupConvGeneral*> algo2gconv;
 
-        std::vector<AlgoBase*>
+    std::vector<AlgoBase*>
             //! all algorithms
             all_algos,
             //! non-cudnn algos, used for heuristic if cudnn is not supported
             non_cudnn_algos;
 
-        AlgoCUDNN* cudnn_from_enum(cudnnConvolutionFwdAlgo_t algo);
+    AlgoCUDNN* cudnn_from_enum(cudnnConvolutionFwdAlgo_t algo);
+
+    const AlgoBase::Mapper& all_algos_map() const { return m_all_algos_map; }
 };
 
-} // namespace cuda
-} // namespace megdnn
+}  // namespace cuda
+}  // namespace megdnn
 
 // vim: syntax=cpp.doxygen

dnn/src/cuda/convolution3d/forward/cudnn.cpp

@@ -78,30 +78,10 @@ void Convolution3DForwardImpl::AlgoCUDNN::exec(
             cudnnGetErrorString(status), args.to_string().c_str());
 }
 
-
 void Convolution3DForwardImpl::AlgoPack::fill_cudnn_algos() {
-#define V1(v) #v
-#define V(v) V1(v)
-
-#define DEF_ALGO(NAME, REPROD) \
-    cudnn.push_back({ \
-                REPROD, #NAME \
-                    "v" V(CUDNN_MAJOR) "." V(CUDNN_MINOR) \
-                    "." V(CUDNN_PATCHLEVEL), \
-                NAME})
-
-DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM, true);
-DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM, true);
-DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING, true);
-
-#if !(CUDNN_MAJOR >= 6 || CUDNN_MINOR >= 1)
-#pragma message "not latest cudnn"
-#endif
-
-#undef DEF_ALGO
-
-#undef V
-#undef V1
+    for (auto&& algo : CudnnAlgoPack::conv3d_fwd_algos()) {
+        cudnn.push_back(algo.first);
+    }
 }
 
 // vim: syntax=cpp.doxygen

dnn/src/cuda/convolution3d/opr_impl.h

@@ -6,7 +6,8 @@
  *
  * 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.
  */
 #pragma once
 
@@ -15,126 +16,155 @@
 namespace megdnn {
 namespace cuda {
 
-class Convolution3DForwardImpl: public Convolution3DForward {
-    public:
-        using Convolution3DForward::Convolution3DForward;
-        void exec(_megdnn_tensor_in src,
-                _megdnn_tensor_in filter,
-                _megdnn_tensor_out dst,
-                _megdnn_workspace workspace) override;
-        std::vector<Algorithm *> get_all_algorithms(const TensorLayout &src,
-                const TensorLayout &filter,
-                const TensorLayout &dst) override;
-        Algorithm* get_algorithm_heuristic(const TensorLayout& src,
-                                           const TensorLayout& filter,
-                                           const TensorLayout& dst,
-                                           size_t workspace_limit_in_bytes,
-                                           bool reproducible) override;
-        Algorithm* get_algorithm_heuristic(const TensorLayout& src,
-                                           const CanonizedFilterMeta& filter,
-                                           const TensorLayout& dst,
-                                           size_t workspace_limit_in_bytes,
-                                           bool reproducible);
-        size_t get_workspace_in_bytes(const TensorLayout& src,
-                                      const TensorLayout& filter,
-                                      const TensorLayout& dst) override;
-        const char* get_algorithm_set_name() const override;
-        class AlgoBase;
-        class AlgoCUDNN;
-        class Algo1x1x1;
-        class AlgoInplaceMatmul;
-        class AlgoChanwise;
-        class AlgoGroupConvGeneral;
-        class AlgoPack;
-        static const AlgoPack& algo_pack() {
-            return sm_algo_pack;
-        }
-    private:
-        static AlgoPack sm_algo_pack;
+class Convolution3DForwardImpl : public Convolution3DForward {
+public:
+    using Convolution3DForward::Convolution3DForward;
+    void exec(_megdnn_tensor_in src, _megdnn_tensor_in filter,
+              _megdnn_tensor_out dst, _megdnn_workspace workspace) override;
+    AlgorithmInfo get_algorithm_info_heuristic(const TensorLayout& src,
+                                       const CanonizedFilterMeta& filter,
+                                       const TensorLayout& dst,
+                                       size_t workspace_limit_in_bytes,
+                                       bool reproducible) {
+        return get_algorithm_heuristic(src, filter, dst,
+                                       workspace_limit_in_bytes, reproducible)
+                ->info();
+    }
+    size_t get_workspace_in_bytes(const TensorLayout& src,
+                                  const TensorLayout& filter,
+                                  const TensorLayout& dst) override;
+    const char* get_algorithm_set_name() const override;
+    class AlgoBase;
+    class AlgoCUDNN;
+    class Algo1x1x1;
+    class AlgoInplaceMatmul;
+    class AlgoChanwise;
+    class AlgoGroupConvGeneral;
+    class AlgoPack;
+    static const AlgoPack& algo_pack() { return sm_algo_pack; }
+    static AlgoBase* get_algo_from_desc(const AlgorithmDesc& desc);
+
+protected:
+    std::vector<Algorithm*> get_all_algorithms(
+            const TensorLayout& src, const TensorLayout& filter,
+            const TensorLayout& dst) override;
+    Algorithm* get_algorithm_heuristic(const TensorLayout& src,
+                                       const TensorLayout& filter,
+                                       const TensorLayout& dst,
+                                       size_t workspace_limit_in_bytes,
+                                       bool reproducible) override;
+
+private:
+    Algorithm* get_algorithm_heuristic(const TensorLayout& src,
+                                       const CanonizedFilterMeta& filter,
+                                       const TensorLayout& dst,
+                                       size_t workspace_limit_in_bytes,
+                                       bool reproducible);
+
+
+    static AlgoPack sm_algo_pack;
 };
 
-class Convolution3DBackwardDataImpl: public Convolution3DBackwardData {
-    public:
-        using Convolution3DBackwardData::Convolution3DBackwardData;
-        void exec(_megdnn_tensor_in filter,
-                _megdnn_tensor_in diff,
-                _megdnn_tensor_out grad,
-                _megdnn_workspace workspace) override;
-        std::vector<Algorithm *> get_all_algorithms(const TensorLayout &filter,
-                const TensorLayout &diff,
-                const TensorLayout &grad) override;
-        Algorithm* get_algorithm_heuristic(const TensorLayout& filter,
-                                           const TensorLayout& diff,
-                                           const TensorLayout& grad,
-                                           size_t workspace_limit_in_bytes,
-                                           bool reproducible) override;
-        Algorithm* get_algorithm_heuristic(const CanonizedFilterMeta& filter,
-                                           const TensorLayout& diff,
-                                           const TensorLayout& grad,
-                                           size_t workspace_limit_in_bytes,
-                                           bool reproducible);
-        size_t get_workspace_in_bytes(const TensorLayout& filter,
-                                      const TensorLayout& diff,
-                                      const TensorLayout& grad) override;
-        const char* get_algorithm_set_name() const override;
-
-        class AlgoBase;
-        class AlgoCUDNN;
-        class AlgoInplaceMatmul;
-        class AlgoChanwise;
-        class AlgoGroupConvGeneral;
-
-        class AlgoPack;
-
-        static const AlgoPack& algo_pack() {
-            return sm_algo_pack;
-        }
-
-    private:
-        static AlgoPack sm_algo_pack;
+class Convolution3DBackwardDataImpl : public Convolution3DBackwardData {
+public:
+    using Convolution3DBackwardData::Convolution3DBackwardData;
+    void exec(_megdnn_tensor_in filter, _megdnn_tensor_in diff,
+              _megdnn_tensor_out grad, _megdnn_workspace workspace) override;
+    AlgorithmInfo get_algorithm_info_heuristic(
+            const CanonizedFilterMeta& filter, const TensorLayout& diff,
+            const TensorLayout& grad, size_t workspace_limit_in_bytes,
+            bool reproducible) {
+        return get_algorithm_heuristic(filter, diff, grad,
+                                       workspace_limit_in_bytes, reproducible)
+                ->info();
+    }
+    size_t get_workspace_in_bytes(const TensorLayout& filter,
+                                  const TensorLayout& diff,
+                                  const TensorLayout& grad) override;
+    const char* get_algorithm_set_name() const override;
+
+    class AlgoBase;
+    class AlgoCUDNN;
+    class AlgoInplaceMatmul;
+    class AlgoChanwise;
+    class AlgoGroupConvGeneral;
+
+    class AlgoPack;
+
+    static const AlgoPack& algo_pack() { return sm_algo_pack; }
+    static AlgoBase* get_algo_from_desc(const AlgorithmDesc& desc);
+
+protected:
+    std::vector<Algorithm*> get_all_algorithms(
+            const TensorLayout& filter, const TensorLayout& diff,
+            const TensorLayout& grad) override;
+    Algorithm* get_algorithm_heuristic(const TensorLayout& filter,
+                                       const TensorLayout& diff,
+                                       const TensorLayout& grad,
+                                       size_t workspace_limit_in_bytes,
+                                       bool reproducible) override;
+
+private:
+    Algorithm* get_algorithm_heuristic(const CanonizedFilterMeta& filter,
+                                       const TensorLayout& diff,
+                                       const TensorLayout& grad,
+                                       size_t workspace_limit_in_bytes,
+                                       bool reproducible);
+
+    static AlgoPack sm_algo_pack;
 };
 
-class Convolution3DBackwardFilterImpl: public Convolution3DBackwardFilter {
-    public:
-        using Convolution3DBackwardFilter::Convolution3DBackwardFilter;
-        void exec(_megdnn_tensor_in src,
-                _megdnn_tensor_in diff,
-                _megdnn_tensor_out grad,
-                _megdnn_workspace workspace) override;
-        std::vector<Algorithm *> get_all_algorithms(const TensorLayout &src,
-                const TensorLayout &diff,
-                const TensorLayout &grad) override;
-        Algorithm* get_algorithm_heuristic(const TensorLayout& src,
-                                           const TensorLayout& diff,
-                                           const TensorLayout& grad,
-                                           size_t workspace_limit_in_bytes,
-                                           bool reproducible) override;
-        Algorithm* get_algorithm_heuristic(const TensorLayout& src,
-                                           const TensorLayout& diff,
-                                           const CanonizedFilterMeta& grad,
-                                           size_t workspace_limit_in_bytes,
-                                           bool reproducible);
-        size_t get_workspace_in_bytes(const TensorLayout& src,
-                                      const TensorLayout& diff,
-                                      const TensorLayout& grad) override;
-        const char* get_algorithm_set_name() const override;
-
-        class AlgoBase;
-        class AlgoCUDNN;
-        class AlgoInplaceMatmul;
-        class AlgoChanwise;
-        class AlgoGroupConvGeneral;
-
-        class AlgoPack;
-
-        static const AlgoPack& algo_pack() {
-            return sm_algo_pack;
-        }
-
-    private:
-        static AlgoPack sm_algo_pack;
+class Convolution3DBackwardFilterImpl : public Convolution3DBackwardFilter {
+public:
+    using Convolution3DBackwardFilter::Convolution3DBackwardFilter;
+    void exec(_megdnn_tensor_in src, _megdnn_tensor_in diff,
+              _megdnn_tensor_out grad, _megdnn_workspace workspace) override;
+    size_t get_workspace_in_bytes(const TensorLayout& src,
+                                  const TensorLayout& diff,
+                                  const TensorLayout& grad) override;
+    AlgorithmInfo get_algorithm_info_heuristic(const TensorLayout& src,
+                                               const TensorLayout& diff,
+                                               const CanonizedFilterMeta& grad,
+                                               size_t workspace_limit_in_bytes,
+                                               bool reproducible) {
+        return get_algorithm_heuristic(src, diff, grad,
+                                       workspace_limit_in_bytes, reproducible)
+                ->info();
+    }
+
+    const char* get_algorithm_set_name() const override;
+
+    class AlgoBase;
+    class AlgoCUDNN;
+    class AlgoInplaceMatmul;
+    class AlgoChanwise;
+    class AlgoGroupConvGeneral;
+
+    class AlgoPack;
+
+    static const AlgoPack& algo_pack() { return sm_algo_pack; }
+    static AlgoBase* get_algo_from_desc(const AlgorithmDesc& desc);
+
+protected:
+    std::vector<Algorithm*> get_all_algorithms(
+            const TensorLayout& src, const TensorLayout& diff,
+            const TensorLayout& grad) override;
+    Algorithm* get_algorithm_heuristic(const TensorLayout& src,
+                                       const TensorLayout& diff,
+                                       const TensorLayout& grad,
+                                       size_t workspace_limit_in_bytes,
+                                       bool reproducible) override;
+
+private:
+    Algorithm* get_algorithm_heuristic(const TensorLayout& src,
+                                       const TensorLayout& diff,
+                                       const CanonizedFilterMeta& grad,
+                                       size_t workspace_limit_in_bytes,
+                                       bool reproducible);
+
+    static AlgoPack sm_algo_pack;
 };
-} // namespace cuda
-} // namespace megdnn
+}  // namespace cuda
+}  // namespace megdnn
 
 // vim: syntax=cpp.doxygen

dnn/src/cuda/cudnn_wrapper.cpp

@@ -433,6 +433,137 @@ void Conv3DDesc::set(const param::Convolution3D& param, const size_t nr_group) {
             desc, 3, padA, filterStrideA, dilationA, mode, CUDNN_DATA_FLOAT));
 }
 
+////////////////////////// CudnnAlgoPack //////////////////////////
+
+#define V1(v) #v
+#define V(v) V1(v)
+#define DEF_NAME(NAME) \
+    #NAME "v" V(CUDNN_MAJOR) "." V(CUDNN_MINOR) "." V(CUDNN_PATCHLEVEL)
+#define DEF_ALGO(NAME, PROD)           \
+    {                                  \
+        NAME, { DEF_NAME(NAME), PROD } \
+    }
+
+#if !(CUDNN_MAJOR >= 6 || CUDNN_MINOR >= 1)
+#pragma message "not latest cudnn"
+#endif
+
+const std::unordered_map<cudnnConvolutionBwdDataAlgo_t, CudnnAlgoPack::Attr>
+CudnnAlgoPack::conv_bwd_data_algos() {
+    static const std::unordered_map<cudnnConvolutionBwdDataAlgo_t,
+                                    CudnnAlgoPack::Attr>
+            algos = {
+                DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_0, false),
+                DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_1, true),
+                DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT, true),
+                DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT_TILING, true),
+#if CUDNN_MAJOR >= 5
+                DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD, true),
+#if CUDNN_MAJOR >= 6 || CUDNN_MINOR >= 1
+                DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED,
+                         true),
+#endif
+#endif
+            };
+
+    return algos;
+}
+
+const std::unordered_map<cudnnConvolutionBwdFilterAlgo_t, CudnnAlgoPack::Attr>
+CudnnAlgoPack::conv_bwd_flt_algos() {
+    static const std::unordered_map<cudnnConvolutionBwdFilterAlgo_t,
+                                    CudnnAlgoPack::Attr>
+            algos = {
+                DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0, false),
+                DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1, true),
+                DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT, true),
+                DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_3, false),
+#if CUDNN_MAJOR >= 6 || (CUDNN_MAJOR >= 5 && CUDNN_MINOR >= 1)
+                DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED,
+                         true),
+#if CUDNN_MAJOR >= 6
+                DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT_TILING, true),
+#endif
+#endif
+
+            };
+
+    return algos;
+}
+
+
+const std::unordered_map<cudnnConvolutionFwdAlgo_t, CudnnAlgoPack::Attr>
+CudnnAlgoPack::conv_fwd_algos() {
+    static const std::unordered_map<cudnnConvolutionFwdAlgo_t,
+                                    CudnnAlgoPack::Attr>
+            algos = {
+                DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM, true),
+                DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM,
+                         true),
+                DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_GEMM, true),
+                DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_DIRECT, true),
+                DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_FFT, true),
+                DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING, true),
+
+#if CUDNN_MAJOR >= 5
+                DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD, true),
+#if CUDNN_MAJOR >= 6 || CUDNN_MINOR >= 1
+                DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED, true),
+#endif
+#endif
+
+            };
+
+    return algos;
+}
+
+const std::unordered_map<cudnnConvolutionBwdDataAlgo_t, CudnnAlgoPack::Attr>
+CudnnAlgoPack::conv3d_bwd_data_algos() {
+    static const std::unordered_map<cudnnConvolutionBwdDataAlgo_t,
+                                    CudnnAlgoPack::Attr>
+            algos = {
+                    DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_0, false),
+                    DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_1, true),
+                    DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT_TILING, true),
+            };
+
+    return algos;
+}  // namespace cuda
+
+const std::unordered_map<cudnnConvolutionBwdFilterAlgo_t, CudnnAlgoPack::Attr>
+CudnnAlgoPack::conv3d_bwd_flt_algos() {
+#pragma message \
+        "fp16 dilated conv with odd size filter, only algo_1 works, need focus on doc"
+    static const std::unordered_map<cudnnConvolutionBwdFilterAlgo_t,
+                                    CudnnAlgoPack::Attr>
+            algos = {
+                    DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0, false),
+                    DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1, true),
+                    DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_3, false),
+            };
+
+    return algos;
+}
+
+const std::unordered_map<cudnnConvolutionFwdAlgo_t, CudnnAlgoPack::Attr>
+CudnnAlgoPack::conv3d_fwd_algos() {
+    static const std::unordered_map<cudnnConvolutionFwdAlgo_t,
+                                    CudnnAlgoPack::Attr>
+            algos = {
+                    DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM, true),
+                    DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM,
+                             true),
+                    DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING, true),
+            };
+
+    return algos;
+}
+
+#undef DEF_ALGO
+#undef DEF_NAME
+#undef V
+#undef V1
+
 }  // namespace cuda
 }  // namespace megdnn
 

dnn/src/cuda/cudnn_wrapper.h

@@ -10,6 +10,7 @@
  */
 #pragma once
 
+#include <unordered_map>
 #include "megdnn/basic_types.h"
 #include "megdnn/oprs/nn.h"
 #include "src/cuda/cudnn_with_check.h"
@@ -27,7 +28,7 @@ class TensorDesc {
     public:
         TensorDesc();
         //! default layout is nchw
-        void set(const TensorLayout& layout, const param::Convolution::Format = 
+        void set(const TensorLayout& layout, const param::Convolution::Format =
                 param::Convolution::Format::NCHW);
         ~TensorDesc();
         cudnnTensorDescriptor_t desc;
@@ -103,9 +104,52 @@ class Conv3DDesc {
         cudnnConvolutionDescriptor_t desc;
 };
 
+class CudnnAlgoPack {
+public:
+    //! algorithm attr
+    struct Attr {
+        std::string name;
+        bool is_reproducible;
+    };
 
+    static const std::unordered_map<cudnnConvolutionBwdDataAlgo_t, Attr>
+    conv_bwd_data_algos();
 
-} // namespace cuda
-} // namespace megdnn
+    static const std::unordered_map<cudnnConvolutionBwdFilterAlgo_t, Attr>
+    conv_bwd_flt_algos();
+
+    static const std::unordered_map<cudnnConvolutionFwdAlgo_t, Attr>
+    conv_fwd_algos();
+
+    static const std::unordered_map<cudnnConvolutionBwdDataAlgo_t, Attr>
+    conv3d_bwd_data_algos();
+
+    static const std::unordered_map<cudnnConvolutionBwdFilterAlgo_t, Attr>
+    conv3d_bwd_flt_algos();
+
+    static const std::unordered_map<cudnnConvolutionFwdAlgo_t, Attr>
+    conv3d_fwd_algos();
+
+};
+
+}  // namespace cuda
+}  // namespace megdnn
+
+namespace std {
+
+#define DEF_HASH(_type)                                                \
+    template <>                                                        \
+    struct hash<_type> {                                               \
+        std::size_t operator()(const _type& algo) const {              \
+            return std::hash<uint32_t>()(static_cast<uint32_t>(algo)); \
+        }                                                              \
+    }
+
+DEF_HASH(cudnnConvolutionBwdDataAlgo_t);
+DEF_HASH(cudnnConvolutionBwdFilterAlgo_t);
+DEF_HASH(cudnnConvolutionFwdAlgo_t);
+
+#undef DEF_HASH
+}  // namespace std
 
 // vim: syntax=cpp.doxygen

dnn/src/cuda/deformable_conv/bwd_data/algo.cpp

@@ -19,7 +19,12 @@ using OprImpl = DeformableConvBackwardDataImpl;
 
 OprImpl::AlgoPack::AlgoPack() {
     all_algos.push_back(&algo_matmul);
+
+    for (auto&& algo : all_algos) {
+        m_all_algos_map.emplace(algo->info().desc, algo);
+    }
 }
+MEGDNN_DEF_GET_ALGO_FROM_DESC(DeformableConvBackwardDataImpl)
 
 OprImpl::AlgoPack OprImpl::sm_algo_pack;
 

dnn/src/cuda/deformable_conv/bwd_data/algo.h

@@ -13,11 +13,15 @@
 
 #include "megdnn/oprs.h"
 
+#include "src/common/algo_base.h"
+#include "src/common/metahelper.h"
 #include "src/common/utils.h"
 #include "src/cuda/handle.h"
 
 #include "src/cuda/deformable_conv/opr_impl.h"
 
+#include <unordered_map>
+
 namespace megdnn {
 namespace cuda {
 
@@ -26,6 +30,10 @@ protected:
     ~AlgoBase() = default;
 
 public:
+    enum class AlgoType : uint32_t {
+        CUDA_MATMUL,
+    };
+    using Mapper = std::unordered_map<AlgorithmDesc, AlgoBase*>;
     AlgoBase() : Algorithm() { m_handle_type = Handle::HandleType::CUDA; }
     struct SizeArgs {
         DeformableConvBackwardDataImpl* opr;
@@ -107,17 +115,18 @@ public:
     bool is_reproducible() const override { return true; }
 
     const char* name() const override { return "AlgoMatmul"; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_MATMUL)
 };
 
-class DeformableConvBackwardDataImpl::AlgoPack {
-    AlgoPack(const AlgoPack&) = delete;
-    AlgoPack& operator=(const AlgoPack&) = delete;
-
+class DeformableConvBackwardDataImpl::AlgoPack : NonCopyableObj {
+    AlgoBase::Mapper m_all_algos_map;
 public:
     AlgoPack();
     AlgoMatmul algo_matmul;
     //! all algorithms
     std::vector<AlgoBase*> all_algos;
+
+    const AlgoBase::Mapper& all_algos_map() const { return m_all_algos_map; }
 };
 
 }  // namespace cuda

dnn/src/cuda/deformable_conv/bwd_flt/algo.cpp

@@ -20,7 +20,11 @@ using OprImpl = DeformableConvBackwardFilterImpl;
 
 OprImpl::AlgoPack::AlgoPack() {
     all_algos.push_back(&algo_matmul);
+    for (auto&& algo : all_algos) {
+        m_all_algos_map.emplace(algo->info().desc, algo);
+    }
 }
+MEGDNN_DEF_GET_ALGO_FROM_DESC(DeformableConvBackwardFilterImpl)
 
 OprImpl::AlgoPack OprImpl::sm_algo_pack;
 

dnn/src/cuda/deformable_conv/bwd_flt/algo.h

@@ -13,11 +13,15 @@
 
 #include "megdnn/oprs.h"
 
+#include "src/common/algo_base.h"
+#include "src/common/metahelper.h"
 #include "src/common/utils.h"
 #include "src/cuda/handle.h"
 
 #include "src/cuda/deformable_conv/opr_impl.h"
 
+#include <unordered_map>
+
 namespace megdnn {
 namespace cuda {
 
@@ -26,6 +30,11 @@ protected:
     ~AlgoBase() = default;
 
 public:
+    enum class AlgoType : uint32_t {
+        CUDA_MATMUL,
+    };
+    using Mapper = std::unordered_map<AlgorithmDesc, AlgoBase*>;
+
     AlgoBase() : Algorithm() { m_handle_type = Handle::HandleType::CUDA; }
     struct SizeArgs {
         DeformableConvBackwardFilterImpl* opr;
@@ -97,18 +106,18 @@ public:
     bool is_reproducible() const override { return true; }
 
     const char* name() const override { return "AlgoMatmul"; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_MATMUL)
 };
 
-class DeformableConvBackwardFilterImpl::AlgoPack {
-    AlgoPack(const AlgoPack&) = delete;
-    AlgoPack& operator=(const AlgoPack&) = delete;
-
+class DeformableConvBackwardFilterImpl::AlgoPack : NonCopyableObj {
+    AlgoBase::Mapper m_all_algos_map;
 public:
     AlgoPack();
 
     AlgoMatmul algo_matmul;
     //! all algorithms
     std::vector<AlgoBase*> all_algos;
+    const AlgoBase::Mapper& all_algos_map() const { return m_all_algos_map; }
 };
 
 }  // namespace cuda

dnn/src/cuda/deformable_conv/fwd/algo.cpp

@@ -22,8 +22,14 @@ using OprImpl = DeformableConvForwardImpl;
 
 OprImpl::AlgoPack::AlgoPack() {
     all_algos.push_back(&algo_matmul);
+
+    for (auto&& algo : all_algos) {
+        m_all_algos_map.emplace(algo->info().desc, algo);
+    }
 }
 
+MEGDNN_DEF_GET_ALGO_FROM_DESC(DeformableConvForwardImpl)
+
 OprImpl::AlgoPack OprImpl::sm_algo_pack;
 
 OprImpl::AlgoBase::SizeArgs::SizeArgs(OprImpl* o, const TensorLayout& im,

dnn/src/cuda/deformable_conv/fwd/algo.h

@@ -13,9 +13,13 @@
 
 #include "megdnn/oprs.h"
 
+#include "src/common/algo_base.h"
+#include "src/common/metahelper.h"
 #include "src/cuda/deformable_conv/opr_impl.h"
 #include "src/cuda/utils.h"
 
+#include <unordered_map>
+
 namespace megdnn {
 namespace cuda {
 
@@ -24,6 +28,11 @@ protected:
     ~AlgoBase() = default;
 
 public:
+    enum class AlgoType : uint32_t {
+        CUDA_MATMUL,
+    };
+    using Mapper = std::unordered_map<AlgorithmDesc, AlgoBase*>;
+
     AlgoBase() : Algorithm() { m_handle_type = Handle::HandleType::CUDA; }
     struct SizeArgs {
         DeformableConvForwardImpl* opr;
@@ -92,17 +101,17 @@ public:
     bool is_reproducible() const override { return true; }
 
     const char* name() const override { return "AlgoMatmul"; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_MATMUL)
 };
 
-class DeformableConvForwardImpl::AlgoPack {
-    AlgoPack(const AlgoPack&) = delete;
-    AlgoPack& operator=(const AlgoPack&) = delete;
-
+class DeformableConvForwardImpl::AlgoPack : NonCopyableObj {
+    AlgoBase::Mapper m_all_algos_map;
 public:
     AlgoPack();
     AlgoMatmul algo_matmul;
     //! all algorithms
     std::vector<AlgoBase*> all_algos;
+    const AlgoBase::Mapper& all_algos_map() const { return m_all_algos_map; }
 };
 
 }  // namespace cuda

dnn/src/cuda/deformable_conv/opr_impl.h

@@ -6,7 +6,8 @@
  *
  * 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.
  */
 #pragma once
 
@@ -29,19 +30,6 @@ public:
                                   const TensorLayout& mask,
                                   const TensorLayout& dst) override;
 
-    std::vector<Algorithm*> get_all_algorithms(
-            const TensorLayout& im, const TensorLayout& filter,
-            const TensorLayout& offset, const TensorLayout& mask,
-            const TensorLayout& dst) override;
-
-    Algorithm* get_algorithm_heuristic(const TensorLayout& im,
-                                       const TensorLayout& filter,
-                                       const TensorLayout& offset,
-                                       const TensorLayout& mask,
-                                       const TensorLayout& dst,
-                                       size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
-
     Algorithm* get_algorithm_heuristic(const TensorLayout& im,
                                        const CanonizedFilterMeta& filter,
                                        const TensorLayout& offset,
@@ -58,31 +46,35 @@ public:
     class AlgoPack;
 
     static const AlgoPack& algo_pack() { return sm_algo_pack; }
+    static AlgoBase* get_algo_from_desc(const AlgorithmDesc& desc);
+
+protected:
+    std::vector<Algorithm*> get_all_algorithms(
+            const TensorLayout& im, const TensorLayout& filter,
+            const TensorLayout& offset, const TensorLayout& mask,
+            const TensorLayout& dst) override;
+
+    Algorithm* get_algorithm_heuristic(const TensorLayout& im,
+                                       const TensorLayout& filter,
+                                       const TensorLayout& offset,
+                                       const TensorLayout& mask,
+                                       const TensorLayout& dst,
+                                       size_t workspace_limit_in_bytes,
+                                       bool reproducible) override;
 
 private:
     static AlgoPack sm_algo_pack;
 };
 
-class DeformableConvBackwardFilterImpl: public DeformableConvBackwardFilter {
+class DeformableConvBackwardFilterImpl : public DeformableConvBackwardFilter {
 public:
     using DeformableConvBackwardFilter::DeformableConvBackwardFilter;
 
-    void exec(_megdnn_tensor_in im,_megdnn_tensor_in offset, _megdnn_tensor_in mask,
-              _megdnn_tensor_in out_grad, _megdnn_tensor_out filter_grad,
+    void exec(_megdnn_tensor_in im, _megdnn_tensor_in offset,
+              _megdnn_tensor_in mask, _megdnn_tensor_in out_grad,
+              _megdnn_tensor_out filter_grad,
               _megdnn_workspace workspace) override;
 
-    std::vector<Algorithm*> get_all_algorithms(
-            const TensorLayout& im, const TensorLayout& offset, const TensorLayout& mask,
-            const TensorLayout& out_grad, const TensorLayout& filter_grad) override;
-
-    Algorithm* get_algorithm_heuristic(const TensorLayout& im,
-                                       const TensorLayout& offset,
-                                       const TensorLayout& mask,
-                                       const TensorLayout& out_grad,
-                                       const TensorLayout& filter_grad,
-                                       size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
-
     Algorithm* get_algorithm_heuristic(const TensorLayout& im,
                                        const TensorLayout& offset,
                                        const TensorLayout& mask,
@@ -91,9 +83,11 @@ public:
                                        size_t workspace_limit_in_bytes,
                                        bool reproducible);
 
-    size_t get_workspace_in_bytes(
-            const TensorLayout& im, const TensorLayout& offset, const TensorLayout& mask,
-            const TensorLayout& out_grad, const TensorLayout& filter_grad) override;
+    size_t get_workspace_in_bytes(const TensorLayout& im,
+                                  const TensorLayout& offset,
+                                  const TensorLayout& mask,
+                                  const TensorLayout& out_grad,
+                                  const TensorLayout& filter_grad) override;
 
     const char* get_algorithm_set_name() const override;
 
@@ -103,6 +97,21 @@ public:
     class AlgoPack;
 
     static const AlgoPack& algo_pack() { return sm_algo_pack; }
+    static AlgoBase* get_algo_from_desc(const AlgorithmDesc& desc);
+
+protected:
+    std::vector<Algorithm*> get_all_algorithms(
+            const TensorLayout& im, const TensorLayout& offset,
+            const TensorLayout& mask, const TensorLayout& out_grad,
+            const TensorLayout& filter_grad) override;
+
+    Algorithm* get_algorithm_heuristic(const TensorLayout& im,
+                                       const TensorLayout& offset,
+                                       const TensorLayout& mask,
+                                       const TensorLayout& out_grad,
+                                       const TensorLayout& filter_grad,
+                                       size_t workspace_limit_in_bytes,
+                                       bool reproducible) override;
 
 private:
     static AlgoPack sm_algo_pack;
@@ -118,19 +127,6 @@ public:
               _megdnn_tensor_out offset_grad, _megdnn_tensor_out mask_grad,
               _megdnn_workspace workspace) override;
 
-    std::vector<Algorithm*> get_all_algorithms(
-            const TensorLayout& im, const TensorLayout& filter,
-            const TensorLayout& offset, const TensorLayout& mask,
-            const TensorLayout& out_grad, const TensorLayout& im_grad,
-            const TensorLayout& offset_grad, const TensorLayout& mask_grad) override;
-
-    Algorithm* get_algorithm_heuristic(
-            const TensorLayout& im, const TensorLayout& filter,
-            const TensorLayout& offset, const TensorLayout& mask,
-            const TensorLayout& out_grad, const TensorLayout& im_grad,
-            const TensorLayout& offset_grad, const TensorLayout& mask_grad,
-            size_t workspace_limit_in_bytes, bool reproducible) override;
-
     Algorithm* get_algorithm_heuristic(
             const TensorLayout& im, const CanonizedFilterMeta& filter,
             const TensorLayout& offset, const TensorLayout& mask,
@@ -138,11 +134,14 @@ public:
             const TensorLayout& offset_grad, const TensorLayout& mask_grad,
             size_t workspace_limit_in_bytes, bool reproducible);
 
-    size_t get_workspace_in_bytes(
-            const TensorLayout& im, const TensorLayout& filter,
-            const TensorLayout& offset, const TensorLayout& mask,
-            const TensorLayout& out_grad, const TensorLayout& im_grad,
-            const TensorLayout& offset_grad, const TensorLayout& mask_grad) override;
+    size_t get_workspace_in_bytes(const TensorLayout& im,
+                                  const TensorLayout& filter,
+                                  const TensorLayout& offset,
+                                  const TensorLayout& mask,
+                                  const TensorLayout& out_grad,
+                                  const TensorLayout& im_grad,
+                                  const TensorLayout& offset_grad,
+                                  const TensorLayout& mask_grad) override;
 
     const char* get_algorithm_set_name() const override;
 
@@ -152,6 +151,22 @@ public:
     class AlgoPack;
 
     static const AlgoPack& algo_pack() { return sm_algo_pack; }
+    static AlgoBase* get_algo_from_desc(const AlgorithmDesc& desc);
+
+protected:
+    std::vector<Algorithm*> get_all_algorithms(
+            const TensorLayout& im, const TensorLayout& filter,
+            const TensorLayout& offset, const TensorLayout& mask,
+            const TensorLayout& out_grad, const TensorLayout& im_grad,
+            const TensorLayout& offset_grad,
+            const TensorLayout& mask_grad) override;
+
+    Algorithm* get_algorithm_heuristic(
+            const TensorLayout& im, const TensorLayout& filter,
+            const TensorLayout& offset, const TensorLayout& mask,
+            const TensorLayout& out_grad, const TensorLayout& im_grad,
+            const TensorLayout& offset_grad, const TensorLayout& mask_grad,
+            size_t workspace_limit_in_bytes, bool reproducible) override;
 
 private:
     static AlgoPack sm_algo_pack;

dnn/src/cuda/local_share/backward_data/algo.cpp

@@ -18,8 +18,14 @@ using namespace cuda;
 LocalShareBackwardDataImpl::AlgoPack::AlgoPack() {
     all_algos.push_back(&implicit_gemm);
     all_algos.push_back(&batched_matmul);
+
+    for (auto&& algo : all_algos) {
+        m_all_algos_map.emplace(algo->info().desc, algo);
+    }
 }
 
+MEGDNN_DEF_GET_ALGO_FROM_DESC(LocalShareBackwardDataImpl)
+
 LocalShareBackwardDataImpl::AlgoPack LocalShareBackwardDataImpl::sm_algo_pack;
 
 LocalShareBackwardDataImpl::AlgoBase::SizeArgs::SizeArgs(

dnn/src/cuda/local_share/backward_data/algo.h

@@ -13,10 +13,14 @@
 
 #include "megdnn/oprs.h"
 
+#include "src/common/algo_base.h"
+#include "src/common/metahelper.h"
 #include "src/common/utils.h"
 #include "src/cuda/handle.h"
 #include "src/cuda/local_share/opr_impl.h"
 
+#include <unordered_map>
+
 namespace megdnn {
 namespace cuda {
 
@@ -25,6 +29,13 @@ protected:
     ~AlgoBase() = default;
 
 public:
+    enum class AlgoType : uint32_t {
+        CUDA_IMPLICIT_GEMM,
+        CUDA_BATCHED_MATMUL,
+    };
+    using Mapper = std::unordered_map<AlgorithmDesc, AlgoBase*>;
+
+
     AlgoBase() : Algorithm() { m_handle_type = Handle::HandleType::CUDA; }
     struct SizeArgs {
         LocalShareBackwardDataImpl* opr;
@@ -77,6 +88,7 @@ public:
     const char* name() const override {
         return "LOCAL_SHARE_IMPLICIT_GEMM";
     }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_IMPLICIT_GEMM)
 };
 
 class LocalShareBackwardDataImpl::AlgoBatchedMatMul final
@@ -93,11 +105,11 @@ public:
     const char* name() const override {
         return "LOCAL_SHARE_BATCHED_MATMUL";
     }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_BATCHED_MATMUL)
 };
 
-class LocalShareBackwardDataImpl::AlgoPack {
-    AlgoPack(const AlgoPack&) = delete;
-    AlgoPack& operator=(const AlgoPack&) = delete;
+class LocalShareBackwardDataImpl::AlgoPack : NonCopyableObj {
+    AlgoBase::Mapper m_all_algos_map;
 
 public:
     AlgoPack();
@@ -106,6 +118,7 @@ public:
     AlgoBatchedMatMul batched_matmul;
 
     std::vector<AlgoBase*> all_algos;
+    const AlgoBase::Mapper& all_algos_map() const { return m_all_algos_map; }
 };
 
 }  // namespace cuda

dnn/src/cuda/local_share/backward_filter/algo.cpp

@@ -18,8 +18,14 @@ using namespace cuda;
 LocalShareBackwardFilterImpl::AlgoPack::AlgoPack() {
     all_algos.push_back(&implicit_gemm);
     all_algos.push_back(&batched_matmul);
+
+    for (auto&& algo : all_algos) {
+        m_all_algos_map.emplace(algo->info().desc, algo);
+    }
 }
 
+MEGDNN_DEF_GET_ALGO_FROM_DESC(LocalShareBackwardFilterImpl)
+
 LocalShareBackwardFilterImpl::AlgoPack LocalShareBackwardFilterImpl::sm_algo_pack;
 
 LocalShareBackwardFilterImpl::AlgoBase::SizeArgs::SizeArgs(

dnn/src/cuda/local_share/backward_filter/algo.h

@@ -13,10 +13,14 @@
 
 #include "megdnn/oprs.h"
 
+#include "src/common/algo_base.h"
+#include "src/common/metahelper.h"
 #include "src/common/utils.h"
 #include "src/cuda/handle.h"
 #include "src/cuda/local_share/opr_impl.h"
 
+#include <unordered_map>
+
 namespace megdnn {
 namespace cuda {
 
@@ -25,6 +29,12 @@ protected:
     ~AlgoBase() = default;
 
 public:
+    enum class AlgoType : uint32_t {
+        CUDA_IMPLICIT_GEMM,
+        CUDA_BATCHED_MATMUL,
+    };
+    using Mapper = std::unordered_map<AlgorithmDesc, AlgoBase*>;
+
     AlgoBase() : Algorithm() { m_handle_type = Handle::HandleType::CUDA; }
     struct SizeArgs {
         LocalShareBackwardFilterImpl* opr;
@@ -75,6 +85,7 @@ public:
     bool is_reproducible() const override { return true; }
 
     const char* name() const override { return "LOCAL_SHARE_IMPLICIT_GEMM"; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_IMPLICIT_GEMM)
 };
 
 class LocalShareBackwardFilterImpl::AlgoBatchedMatMul final : public AlgoBase {
@@ -88,11 +99,11 @@ public:
     bool is_reproducible() const override { return true; }
 
     const char* name() const override { return "LOCAL_SHARE_BATCHED_MATMUL"; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_BATCHED_MATMUL)
 };
 
-class LocalShareBackwardFilterImpl::AlgoPack {
-    AlgoPack(const AlgoPack&) = delete;
-    AlgoPack& operator=(const AlgoPack&) = delete;
+class LocalShareBackwardFilterImpl::AlgoPack : NonCopyableObj {
+    AlgoBase::Mapper m_all_algos_map;
 
 public:
     AlgoPack();
@@ -101,6 +112,8 @@ public:
     AlgoBatchedMatMul batched_matmul;
 
     std::vector<AlgoBase*> all_algos;
+
+    const AlgoBase::Mapper& all_algos_map() const { return m_all_algos_map; }
 };
 
 }  // namespace cuda

dnn/src/cuda/local_share/forward/algo.cpp

@@ -19,8 +19,14 @@ LocalShareForwardImpl::AlgoPack::AlgoPack() {
     all_algos.push_back(&batch_size_aware_chwn_small_image);
     all_algos.push_back(&batch_size_aware_chwn);
     all_algos.push_back(&batched_matmul);
+
+    for (auto&& algo : all_algos) {
+        m_all_algos_map.emplace(algo->info().desc, algo);
+    }
 }
 
+MEGDNN_DEF_GET_ALGO_FROM_DESC(LocalShareForwardImpl)
+
 LocalShareForwardImpl::AlgoPack LocalShareForwardImpl::sm_algo_pack;
 
 LocalShareForwardImpl::AlgoBase::SizeArgs::SizeArgs(LocalShareForwardImpl* o,

dnn/src/cuda/local_share/forward/algo.h

@@ -14,9 +14,13 @@
 #include "megdnn/oprs.h"
 
 #include "src/common/utils.h"
+#include "src/common/algo_base.h"
+#include "src/common/metahelper.h"
 #include "src/cuda/handle.h"
 #include "src/cuda/local_share/opr_impl.h"
 
+#include <unordered_map>
+
 namespace megdnn {
 namespace cuda {
 
@@ -25,6 +29,13 @@ protected:
     ~AlgoBase() = default;
 
 public:
+    enum class AlgoType : uint32_t {
+        CUDA_CHWN_BATCH_SIZE_AWARE,
+        CUDA_CHWN_BATCH_SIZE_AWARE_SMALL_IMAGE,
+        CUDA_BATCHED_MATMUL
+    };
+    using Mapper = std::unordered_map<AlgorithmDesc, AlgoBase*>;
+
     AlgoBase() : Algorithm() { m_handle_type = Handle::HandleType::CUDA; }
     struct SizeArgs {
         LocalShareForwardImpl* opr;
@@ -79,6 +90,7 @@ public:
     const char* name() const override {
         return "LOCAL_SHARE_CHWN_BATCH_SIZE_AWARE";
     }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_CHWN_BATCH_SIZE_AWARE)
 };
 
 class LocalShareForwardImpl::AlgoCHWNBatchSizeAwareSmallImage final
@@ -95,6 +107,7 @@ public:
     const char* name() const override {
         return "LOCAL_SHARE_CHWN_BATCH_SIZE_AWARE_SMALL_IMAGE";
     }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_CHWN_BATCH_SIZE_AWARE_SMALL_IMAGE)
 };
 
 class LocalShareForwardImpl::AlgoBatchedMatMul final : public AlgoBase {
@@ -108,11 +121,11 @@ public:
     bool is_reproducible() const override { return true; }
 
     const char* name() const override { return "LOCAL_SHARE_BATCHED_MATMUL"; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_BATCHED_MATMUL)
 };
 
-class LocalShareForwardImpl::AlgoPack {
-    AlgoPack(const AlgoPack&) = delete;
-    AlgoPack& operator=(const AlgoPack&) = delete;
+class LocalShareForwardImpl::AlgoPack : NonCopyableObj {
+    AlgoBase::Mapper m_all_algos_map;
 
 public:
     AlgoPack();
@@ -122,6 +135,7 @@ public:
     AlgoBatchedMatMul batched_matmul;
 
     std::vector<AlgoBase*> all_algos;
+    const AlgoBase::Mapper& all_algos_map() const { return m_all_algos_map; }
 };
 
 }  // namespace cuda

dnn/src/cuda/local_share/opr_impl.h

@@ -23,14 +23,6 @@ public:
     size_t get_workspace_in_bytes(const TensorLayout& src,
                                   const TensorLayout& filter,
                                   const TensorLayout& dst) override;
-    std::vector<Algorithm*> get_all_algorithms(
-            const TensorLayout& src, const TensorLayout& filter,
-            const TensorLayout& dst) override;
-    Algorithm* get_algorithm_heuristic(const TensorLayout& src,
-                                       const TensorLayout& filter,
-                                       const TensorLayout& dst,
-                                       size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
     const char* get_algorithm_set_name() const override;
 
     class AlgoBase;
@@ -41,7 +33,17 @@ public:
     class AlgoPack;
 
     static const AlgoPack& algo_pack() { return sm_algo_pack; }
+    static AlgoBase* get_algo_from_desc(const AlgorithmDesc& desc);
 
+protected:
+    std::vector<Algorithm*> get_all_algorithms(
+            const TensorLayout& src, const TensorLayout& filter,
+            const TensorLayout& dst) override;
+    Algorithm* get_algorithm_heuristic(const TensorLayout& src,
+                                       const TensorLayout& filter,
+                                       const TensorLayout& dst,
+                                       size_t workspace_limit_in_bytes,
+                                       bool reproducible) override;
 private:
     static AlgoPack sm_algo_pack;
 };
@@ -54,14 +56,6 @@ public:
     size_t get_workspace_in_bytes(const TensorLayout& filter,
                                   const TensorLayout& diff,
                                   const TensorLayout& grad) override;
-    std::vector<Algorithm*> get_all_algorithms(
-            const TensorLayout& filter, const TensorLayout& diff,
-            const TensorLayout& grad) override;
-    Algorithm* get_algorithm_heuristic(const TensorLayout& filter,
-                                       const TensorLayout& diff,
-                                       const TensorLayout& grad,
-                                       size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
     const char* get_algorithm_set_name() const override;
 
     class AlgoBase;
@@ -71,6 +65,17 @@ public:
     class AlgoPack;
 
     static const AlgoPack& algo_pack() { return sm_algo_pack; }
+    static AlgoBase* get_algo_from_desc(const AlgorithmDesc& desc);
+
+protected:
+    std::vector<Algorithm*> get_all_algorithms(
+            const TensorLayout& filter, const TensorLayout& diff,
+            const TensorLayout& grad) override;
+    Algorithm* get_algorithm_heuristic(const TensorLayout& filter,
+                                       const TensorLayout& diff,
+                                       const TensorLayout& grad,
+                                       size_t workspace_limit_in_bytes,
+                                       bool reproducible) override;
 
 private:
     static AlgoPack sm_algo_pack;
@@ -84,14 +89,6 @@ public:
     size_t get_workspace_in_bytes(const TensorLayout& src,
                                   const TensorLayout& diff,
                                   const TensorLayout& grad) override;
-    std::vector<Algorithm*> get_all_algorithms(
-            const TensorLayout& src, const TensorLayout& diff,
-            const TensorLayout& grad) override;
-    Algorithm* get_algorithm_heuristic(const TensorLayout& src,
-                                       const TensorLayout& diff,
-                                       const TensorLayout& grad,
-                                       size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
     const char* get_algorithm_set_name() const override;
 
     class AlgoBase;
@@ -101,6 +98,17 @@ public:
     class AlgoPack;
 
     static const AlgoPack& algo_pack() { return sm_algo_pack; }
+    static AlgoBase* get_algo_from_desc(const AlgorithmDesc& desc);
+
+protected:
+    std::vector<Algorithm*> get_all_algorithms(
+            const TensorLayout& src, const TensorLayout& diff,
+            const TensorLayout& grad) override;
+    Algorithm* get_algorithm_heuristic(const TensorLayout& src,
+                                       const TensorLayout& diff,
+                                       const TensorLayout& grad,
+                                       size_t workspace_limit_in_bytes,
+                                       bool reproducible) override;
 
 private:
     static AlgoPack sm_algo_pack;

dnn/src/cuda/matrix_mul/algos.cpp

@@ -11,6 +11,7 @@
 
 #include "./algos.h"
 #include "src/cuda/utils.h"
+#include "src/common/algo_base.h"
 
 #include <cuda.h>
 #if CUDA_VERSION >= 10010
@@ -33,10 +34,16 @@ MatrixMulForwardImpl::AlgoPack::AlgoPack() {
     cublas_bfloat16 = std::make_unique<AlgoBFloat16>(&cublas);
     all_algos.push_back(cublas_bfloat16.get());
 #endif
+
+    for (auto&& algo : all_algos) {
+        m_all_algos_map.emplace(algo->info().desc, algo);
+    }
 }
 
 MatrixMulForwardImpl::AlgoPack MatrixMulForwardImpl::sm_algo_pack;
 
+MEGDNN_DEF_GET_ALGO_FROM_DESC(MatrixMulForwardImpl)
+
 MatrixMulForwardImpl::AlgoBase::SizeArgs::SizeArgs(MatrixMulForwardImpl* o,
                                                    const TensorLayout& A,
                                                    const TensorLayout& B,
@@ -67,4 +74,5 @@ std::string MatrixMulForwardImpl::AlgoBase::SizeArgs::to_string() const {
             m, k, k, n, m, n, param.transposeA, param.transposeB,
             layout_a.stride[0], layout_b.stride[0], layout_c.stride[0]));
 }
+
 // vim: syntax=cpp.doxygen

dnn/src/cuda/matrix_mul/algos.h

@@ -6,14 +6,18 @@
  *
  * 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.
  */
 
 #pragma once
 #include "megdnn/oprs.h"
 #include "src/common/utils.h"
 #include "src/cuda/matrix_mul/opr_impl.h"
+#include "src/common/algo_base.h"
+#include "src/common/metahelper.h"
 
+#include <unordered_map>
 #include <cuda.h>
 #include <memory>
 #if CUDA_VERSION >= 10010
@@ -32,6 +36,15 @@ protected:
     ~AlgoBase() = default;
 
 public:
+    enum class AlgoType : uint32_t {
+        CUDA_CUBLAS,
+        CUDA_WMMA_UINT4X4X32,
+        CUDA_CUBLASLT,
+        CUDA_NAIVE,
+        CUDA_BFLOAT16
+    };
+    using Mapper = std::unordered_map<AlgorithmDesc, AlgoBase*>;
+
     AlgoBase() : Algorithm() { m_handle_type = Handle::HandleType::CUDA; }
     struct SizeArgs {
         MatrixMulForwardImpl* opr;
@@ -62,12 +75,12 @@ public:
     virtual size_t get_workspace_in_bytes(const SizeArgs& args) const = 0;
     virtual void exec(const ExecArgs& args) const = 0;
 
-    bool is_available_wk(const SizeArgs& args, size_t limit) {
+    bool is_available_wk(const SizeArgs& args, size_t limit) const {
         return is_available(args) && get_workspace_in_bytes(args) <= limit;
     }
     bool is_available_reproducible(
             const SizeArgs& args, bool reproducible = true,
-            size_t limit = std::numeric_limits<size_t>::max()) {
+            size_t limit = std::numeric_limits<size_t>::max()) const {
         return (!reproducible || is_reproducible()) &&
                is_available_wk(args, limit);
     }
@@ -80,8 +93,6 @@ public:
                 name(), req, workspace.size);
         return *this;
     }
-
-
 };
 
 class MatrixMulForwardImpl::AlgoCuBlas final : public AlgoBase {
@@ -91,13 +102,10 @@ public:
     size_t get_workspace_in_bytes(const SizeArgs& /* args */) const override {
         return 0_z;
     }
-    const char* name() const override {
-        return "CUBLAS";
-    }
+    const char* name() const override { return "CUBLAS"; }
     void exec(const ExecArgs& args) const override;
-    bool is_reproducible() const override {
-        return true;
-    }
+    bool is_reproducible() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_CUBLAS)
 };
 
 #if CUDA_VERSION >= 10000
@@ -106,13 +114,10 @@ public:
     AlgoUInt4x4x32WMMA() = default;
     bool is_available(const SizeArgs& args) const override;
     size_t get_workspace_in_bytes(const SizeArgs& args) const override;
-    const char* name() const override {
-        return "UINT4x4x32_WMMA";
-    }
+    const char* name() const override { return "UINT4x4x32_WMMA"; }
     void exec(const ExecArgs& args) const override;
-    bool is_reproducible() const override {
-        return true;
-    }
+    bool is_reproducible() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_WMMA_UINT4X4X32)
 };
 #endif
 #if CUDA_VERSION >= 10010
@@ -120,13 +125,10 @@ class MatrixMulForwardImpl::AlgoCuBlasLt final : public AlgoBase {
 public:
     bool is_available(const SizeArgs& args) const override;
     size_t get_workspace_in_bytes(const SizeArgs& args) const override;
-    const char* name() const override {
-        return "CUBLAS_LT";
-    }
+    const char* name() const override { return "CUBLAS_LT"; }
     void exec(const ExecArgs& args) const override;
-    bool is_reproducible() const override {
-        return true;
-    }
+    bool is_reproducible() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_CUBLASLT)
 };
 #endif
 
@@ -140,6 +142,7 @@ public:
     const char* name() const override { return "NAIVE"; }
     void exec(const ExecArgs& args) const override;
     bool is_reproducible() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_NAIVE)
 };
 
 #if !MEGDNN_DISABLE_FLOAT16
@@ -151,6 +154,13 @@ public:
     const char* name() const override { return m_name.c_str(); }
     void exec(const ExecArgs& args) const override;
     bool is_reproducible() const override { return true; }
+    MEGDNN_DECL_ALGO_TYPE(CUDA_NAIVE)
+
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_algorithm, ret);
+        return ret;
+    }
 
 private:
     MatrixMulForwardImpl::AlgoBase* m_algorithm = nullptr;
@@ -160,9 +170,9 @@ private:
 };
 #endif
 
-class MatrixMulForwardImpl::AlgoPack {
-    AlgoPack(const AlgoPack&) = delete;
-    AlgoPack& operator=(const AlgoPack&) = delete;
+class MatrixMulForwardImpl::AlgoPack : NonCopyableObj {
+private:
+    AlgoBase::Mapper m_all_algos_map;
 
 public:
     AlgoPack();
@@ -178,6 +188,8 @@ public:
     std::unique_ptr<AlgoBFloat16> cublas_bfloat16;
 #endif
     std::vector<AlgoBase*> all_algos;
+
+    const AlgoBase::Mapper& all_algos_map() const { return m_all_algos_map; }
 };
 
 }  // namespace cuda

dnn/src/cuda/matrix_mul/bfloat16.cpp

@@ -82,7 +82,7 @@ void MatrixMulForwardImpl::AlgoBFloat16::exec(const ExecArgs& args) const {
                 args.opr->handle()->create_operator<MatrixMulForward>();
         matmul_opr->param() = args.opr->param();
         matmul_opr->param().compute_mode = Param::ComputeMode::DEFAULT;
-        matmul_opr->execution_policy() = {m_algorithm};
+        matmul_opr->execution_policy() = {m_algorithm->info()};
         matmul_opr->exec(a, b, c, ctypecvt.workspace());
     }
     ctypecvt.comp_to_dst_type(c, args.tensor_c);

dnn/src/cuda/matrix_mul/opr_impl.h

@@ -25,15 +25,6 @@ public:
 
     bool is_thread_safe() const override { return true; }
 
-    std::vector<Algorithm*> get_all_algorithms(const TensorLayout& A,
-                                               const TensorLayout& B,
-                                               const TensorLayout& C) override;
-    Algorithm* get_algorithm_heuristic(const TensorLayout& A,
-                                       const TensorLayout& B,
-                                       const TensorLayout& C,
-                                       size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
-
     const char* get_algorithm_set_name() const override {
         return "CUDA MATMUL";
     }
@@ -55,6 +46,17 @@ public:
     static const AlgoPack& algo_pack() {
         return sm_algo_pack;
     }
+    static AlgoBase* get_algo_from_desc(const AlgorithmDesc& desc);
+
+protected:
+    std::vector<Algorithm*> get_all_algorithms(const TensorLayout& A,
+                                               const TensorLayout& B,
+                                               const TensorLayout& C) override;
+    Algorithm* get_algorithm_heuristic(const TensorLayout& A,
+                                       const TensorLayout& B,
+                                       const TensorLayout& C,
+                                       size_t workspace_limit_in_bytes,
+                                       bool reproducible) override;
 
 private:
     static AlgoPack sm_algo_pack;

dnn/src/fallback/conv_bias/algos.cpp

@@ -10,10 +10,14 @@
  */
 
 #include "src/fallback/conv_bias/algos.h"
+#include "src/fallback/conv_bias/conv1x1/algos.h"
+#include "src/fallback/conv_bias/conv1x1/algos_conv1x1_gemv.h"
+#include "src/fallback/conv_bias/im2col/algos.h"
 #include "megdnn/opr_param_defs.h"
 #include "src/common/opr_delegate.h"
 #include "src/fallback/conv_bias/winograd/strategy.h"
 #include "src/naive/convolution/helper.h"
+#include "src/common/algo_base.h"
 
 #include "midout.h"
 
@@ -176,6 +180,7 @@ void kern_default(const ConvBiasImpl::NCBKernParam& p) {
 }  // namespace
 
 MIDOUT_DECL(megdnn_fallback_naive)
+
 /* ======================= AlgoNaive ======================== */
 
 bool ConvBiasImpl::AlgoNaive::usable(

dnn/src/fallback/conv_bias/algos.h

@@ -36,6 +36,7 @@ public:
                 static_cast<uint32_t>(AlgoDataType::QUINT8X8X32));
         return {support_data_type, AlgoCategory::NAIVE};
     }
+    MEGDNN_DECL_ALGO_TYPE(FB_NAIVE)
 };
 
 class ConvBiasImpl::AlgoWinogradF32 final : public AlgoBase {
@@ -59,6 +60,12 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::FLOAT32, AlgoCategory::WINOGRAD};
     }
+    MEGDNN_DECL_ALGO_TYPE(FB_WINOGRAD_F32)
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_matmul_algo, ret);
+        return ret;
+    }
 
 private:
     MatrixMulImpl::AlgoBase* m_matmul_algo;
@@ -87,6 +94,12 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::FLOAT32, AlgoCategory::WINOGRAD};
     }
+    MEGDNN_DECL_ALGO_TYPE(FB_WINOGRAD_4X4_F32)
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_matmul_algo, ret);
+        return ret;
+    }
 
 private:
     MatrixMulImpl::AlgoBase* m_matmul_algo;
@@ -115,6 +128,12 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::QINT8X8X32, AlgoCategory::WINOGRAD};
     }
+    MEGDNN_DECL_ALGO_TYPE(FB_WINOGRAD_QS8)
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_matmul_algo, ret);
+        return ret;
+    }
 
 private:
     MatrixMulImpl::AlgoBase* m_matmul_algo;
@@ -143,6 +162,12 @@ public:
     ConvAlgoTypePack get_algo_type() const override {
         return {AlgoDataType::QINT8X8X32, AlgoCategory::WINOGRAD};
     }
+    MEGDNN_DECL_ALGO_TYPE(FB_WINOGRAD_8X8_QS8)
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_matmul_algo, ret);
+        return ret;
+    }
 
 private:
     MatrixMulImpl::AlgoBase* m_matmul_algo;

dnn/src/fallback/conv_bias/common.h

@@ -155,6 +155,12 @@ using BiasMode = ConvBiasForward::BiasMode;
             const NCBKernSizeParam& param) const override;                     \
     ConvAlgoTypePack get_algo_type() const override {                          \
         return {_algo_data_type, AlgoCategory::WINOGRAD};                      \
+    }                                                                          \
+    std::string param() const override {                                       \
+        std::string ret;                                                       \
+        serialize_write_pod(m_matmul_algo, ret);                               \
+        serialize_write_pod(m_tile_size, ret);                                 \
+        return ret;                                                            \
     }                                                                          \
                                                                                \
 private:                                                                       \

dnn/src/fallback/conv_bias/conv1x1/algos.h

@@ -60,6 +60,13 @@ public:
         return {m_matmul_algo->matmul_description().algo_type.data_type,
                 AlgoCategory::IM2COL};
     }
+    MEGDNN_DECL_ALGO_TYPE(FB_WINOGRAD_8X8_QS8)
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_matmul_algo, ret);
+        serialize_write_pod(m_oc_block_size, ret);
+        return ret;
+    }
 
 protected:
     size_t get_oc_tile_size_heuristic(const NCBKernSizeParam& param) const;

dnn/src/fallback/conv_bias/conv1x1/algos_conv1x1_gemv.h

@@ -43,6 +43,7 @@ public:
                 static_cast<uint32_t>(AlgoDataType::QUINT8X8X32));
         return {support_data_type, AlgoCategory::IM2COL};
     }
+    MEGDNN_DECL_ALGO_TYPE(FB_CONV1x1_GEMV)
 
 protected:
     size_t get_oc_tile_size_heuristic(const NCBKernSizeParam& param) const;

dnn/src/fallback/conv_bias/im2col/algos.h

@@ -68,6 +68,14 @@ public:
         return {m_matmul_algo->matmul_description().algo_type.data_type,
                 AlgoCategory::IM2COL};
     }
+    MEGDNN_DECL_ALGO_TYPE(FB_IM2COL)
+
+    std::string param() const override {
+        std::string ret;
+        serialize_write_pod(m_matmul_algo, ret);
+        serialize_write_pod(m_ohw_tile_size, ret);
+        return ret;
+    }
 
 private:
     MatrixMulImpl::AlgoBase* m_matmul_algo;

dnn/src/fallback/conv_bias/opr_impl.cpp

@@ -22,6 +22,14 @@
 #include "src/naive/convolution/algorithms.h"
 #include "src/naive/handle.h"
 
+#if MEGDNN_X86
+#include "src/x86/conv_bias/opr_impl.h"
+#elif MEGDNN_AARCH64
+#include "src/aarch64/conv_bias/opr_impl.h"
+#elif MEGDNN_ARMV7
+#include "src/armv7/conv_bias/opr_impl.h"
+#endif
+
 #include <cstring>
 
 using namespace megdnn;
@@ -65,17 +73,19 @@ void incr_ptr(T*& dst, ptrdiff_t delta) {
 class ConvBiasImpl::AlgoPack : NonCopyableObj {
     AlgoNaive algo_naive;
     SmallVector<std::unique_ptr<AlgoBase>> refhold;
+    SmallVector<AlgoBase*> m_all_algos;
+    AlgoBase::Mapper m_all_algos_map;
 
 public:
 
     AlgoPack() {
         refhold.emplace_back(new AlgoConv1x1Gemv());
-        all_algos.emplace_back(refhold.back().get());
+        m_all_algos.emplace_back(refhold.back().get());
 
         static CpuOprDelegationStorage<> storage;
         auto matmul_opr = storage.get<MatrixMul>();
-        auto&& matmul_algos =
-                static_cast<fallback::MatrixMulImpl*>(matmul_opr)->algo_pack();
+        auto&& matmul_algos = static_cast<fallback::MatrixMulImpl*>(matmul_opr)
+                                      ->get_all_packed_algo();
         for (auto&& algo : matmul_algos) {
 #if MEGDNN_X86
 //! As we haven't direct conv for int8x8x16 yet, if we disable gemv here, it may
@@ -97,13 +107,13 @@ public:
                 refhold.emplace_back(new AlgoIm2col(
                         static_cast<MatrixMulImpl::AlgoBase*>(algo),
                         ohw_tile_size));
-                all_algos.emplace_back(refhold.back().get());
+                m_all_algos.emplace_back(refhold.back().get());
             }
             for (size_t oc_tile_size : {48, 24}) {
                 refhold.emplace_back(new AlgoConv1x1(
                         static_cast<MatrixMulImpl::AlgoBase*>(algo),
                         oc_tile_size));
-                all_algos.emplace_back(refhold.back().get());
+                m_all_algos.emplace_back(refhold.back().get());
             }
 #endif
 
@@ -113,26 +123,35 @@ public:
         //! FIXME: I do not know a better way to do it.
             refhold.emplace_back(new AlgoWinogradF32(
                     static_cast<MatrixMulImpl::AlgoBase*>(algo)));
-            all_algos.emplace_back(refhold.back().get());
+            m_all_algos.emplace_back(refhold.back().get());
             refhold.emplace_back(new AlgoWinogradF32_4x4(
                     static_cast<MatrixMulImpl::AlgoBase*>(algo)));
-            all_algos.emplace_back(refhold.back().get());
+            m_all_algos.emplace_back(refhold.back().get());
             refhold.emplace_back(new AlgoWinogradQS8(
                     static_cast<MatrixMulImpl::AlgoBase*>(algo)));
-            all_algos.emplace_back(refhold.back().get());
+            m_all_algos.emplace_back(refhold.back().get());
             refhold.emplace_back(new AlgoWinogradQS8_8x8(
                     static_cast<MatrixMulImpl::AlgoBase*>(algo)));
-            all_algos.emplace_back(refhold.back().get());
+            m_all_algos.emplace_back(refhold.back().get());
 #endif
         }
-        all_algos.emplace_back(&algo_naive);
+        m_all_algos.emplace_back(&algo_naive);
+
+        for (auto&& algo : m_all_algos) {
+            m_all_algos_map.emplace(algo->info().desc, algo);
+        }
     }
-    SmallVector<AlgoBase*> all_algos;
+    const SmallVector<AlgoBase*>& all_algos() const { return m_all_algos; }
+    const AlgoBase::Mapper& all_algos_map() const { return m_all_algos_map; }
 };
 
-SmallVector<ConvBiasImpl::AlgoBase*> ConvBiasImpl::algo_pack() {
-    static AlgoPack sl_algo_pack;
-    return sl_algo_pack.all_algos;
+const ConvBiasImpl::AlgoPack& ConvBiasImpl::algo_pack() {
+    static AlgoPack algo_pack;
+    return algo_pack;
+}
+
+SmallVector<ConvBiasImpl::AlgoBase*> ConvBiasImpl::get_all_packed_algo() {
+    return algo_pack().all_algos();
 }
 
 SmallVector<ConvBiasImpl::AlgoBase*> ConvBiasImpl::select_algo_type(
@@ -140,7 +159,7 @@ SmallVector<ConvBiasImpl::AlgoBase*> ConvBiasImpl::select_algo_type(
     megdnn_assert(nr_type_contain(target_type.data_type),
                   "ConvBias algo selection only support one type");
     SmallVector<ConvBiasImpl::AlgoBase*> algos;
-    for (auto&& algo : algo_pack()) {
+    for (auto&& algo : get_all_packed_algo()) {
         auto algo_type = algo->get_algo_type();
         if (contain_data_type(algo_type.data_type, target_type.data_type) &&
             algo_type.algo_category == target_type.algo_category) {
@@ -166,7 +185,7 @@ void ConvBiasImpl::exec(_megdnn_tensor_in src, _megdnn_tensor_in filter,
                workspace.size, preprocessed_filter);
     auto fparam = make_ncb_kern_param(src, filter, bias, dst, workspace,
                                       preprocessed_filter);
-    ConvBiasImpl::Algorithm* algo = get_algorithm(fparam, workspace.size);
+    auto&& algo = get_algorithm(fparam, workspace.size);
     if (!is_naive_algo(algo) &&
         NCB_ALGO_FUNC(get_workspace, algo, fparam) <= workspace.size) {
         exec_with_ncb_kern(fparam, algo);
@@ -189,9 +208,10 @@ void ConvBiasImpl::exec_preprocess(const TensorLayout& src_layout,
     auto fparam = make_ncb_kern_param(src, filter, bias, dst, workspace,
                                       preprocessed_filter);
     //! should not pass workspace_size limit otherwise can not find match algo
-    ConvBiasImpl::Algorithm* algo = get_algorithm(fparam);
-    if (!is_naive_algo(algo) && NCB_ALGO_FUNC(get_preprocess_workspace, algo,
-                                              fparam) <= workspace.size) {
+    auto&& algo = get_algorithm(fparam);
+    if (!is_naive_algo(algo) &&
+        NCB_ALGO_FUNC(get_preprocess_workspace, algo, fparam) <=
+                workspace.size) {
         exec_preprocess_with_ncb_kern(fparam, algo);
     } else {
         naive::ConvBiasForwardImpl::exec_preprocess(
@@ -207,7 +227,7 @@ size_t ConvBiasImpl::get_workspace_in_bytes(
         const PreprocessedFilter* preprocessed_filter) {
     auto fparam = make_ncb_kern_size_param(src, filter, bias, dst,
                                            preprocessed_filter);
-    ConvBiasImpl::Algorithm* algo = get_algorithm(fparam);
+    auto&& algo = get_algorithm(fparam);
     if (is_naive_algo(algo)) {
         return naive::ConvBiasForwardImpl::get_workspace_in_bytes(
                 src, filter, bias, z, dst, preprocessed_filter);
@@ -221,7 +241,7 @@ size_t ConvBiasImpl::get_preprocess_workspace_in_bytes(
         const TensorLayout& bias, const TensorLayout& z,
         const TensorLayout& dst) {
     auto fparam = make_ncb_kern_size_param(src, filter, bias, dst, nullptr);
-    Algorithm* algo = get_algorithm(fparam);
+    auto&& algo = get_algorithm(fparam);
     if (is_naive_algo(algo)) {
         return naive::ConvBiasForwardImpl::get_preprocess_workspace_in_bytes(
                 src, filter, bias, z, dst);
@@ -235,7 +255,7 @@ SmallVector<TensorLayout> ConvBiasImpl::deduce_preprocessed_filter_layout(
         const TensorLayout& bias, const TensorLayout& z,
         const TensorLayout& dst) {
     auto fparam = make_ncb_kern_size_param(src, filter, bias, dst, nullptr);
-    Algorithm* algo = get_algorithm(fparam);
+    auto&& algo = get_algorithm(fparam);
     if (is_naive_algo(algo)) {
         return naive::ConvBiasForwardImpl::deduce_preprocessed_filter_layout(
                 src, filter, bias, z, dst);
@@ -443,7 +463,7 @@ std::vector<ConvBiasImpl::Algorithm*> ConvBiasImpl::get_all_algorithms_with_ncb(
     MEGDNN_MARK_USED_VAR(param);
     std::vector<Algorithm*> algos;
     std::vector<Algorithm*> prefer_algos;
-    for (auto&& algo : algo_pack()) {
+    for (auto&& algo : get_all_packed_algo()) {
         if (algo->usable(param, AlgoSelectionStrategy::FULL_RUN)) {
             if (algo->is_preferred(param)) {
                 prefer_algos.push_back(algo);
@@ -457,10 +477,49 @@ std::vector<ConvBiasImpl::Algorithm*> ConvBiasImpl::get_all_algorithms_with_ncb(
     return algos;
 }
 
+ConvBiasImpl::Algorithm* ConvBiasImpl::get_algo_from_desc(
+        const AlgorithmDesc& desc) const {
+    if (!desc.valid()) {
+        return nullptr;
+    } else {
+        switch (desc.handle_type) {
+            case Handle::HandleType::FALLBACK: {
+                const auto& map = algo_pack().all_algos_map();
+                megdnn_assert(map.find(desc) != map.end());
+                return map.at(desc);
+            };
+
+#if MEGDNN_X86
+            case Handle::HandleType::X86:
+                return x86::ConvBiasImpl::get_algo_from_desc(desc);
+#elif MEGDNN_AARCH64 || MEGDNN_ARMV7
+            case Handle::HandleType::ARM_COMMON:
+                return arm_common::ConvBiasImpl::get_algo_from_desc(desc);
+#if MEGDNN_AARCH64
+            case Handle::HandleType::AARCH64:
+                return aarch64::ConvBiasImpl::get_algo_from_desc(desc);
+#else
+            case Handle::HandleType::ARMV7:
+                return armv7::ConvBiasImpl::get_algo_from_desc(desc);
+#endif
+#endif
+            case Handle::HandleType::NAIVE: {
+                auto algo = static_cast<naive::HandleImpl*>(handle())
+                                    ->default_conv_bias_fwd_algo();
+                megdnn_assert(algo->info().desc == desc);
+                return algo;
+            }
+            default:
+                megdnn_throw("Unknown handle type");
+                return nullptr;
+        }
+    }
+}
+
 ConvBiasImpl::Algorithm* ConvBiasImpl::get_algorithm(
         const NCBKernSizeParam& param, size_t workspace_size) {
-    if (auto set = execution_policy().algorithm) {
-        return set;
+    if (auto algo = get_algo_from_desc(execution_policy().algo.desc)) {
+        return algo;
     }
     if (!m_prev_selected_algo ||
         memcmp(&m_prev_selected_algo_sizep, &param, sizeof(NCBKernSizeParam))) {

dnn/src/fallback/conv_bias/opr_impl.h

@@ -216,6 +216,86 @@ public:
         AlgoBase() : Algorithm() {
             m_handle_type = Handle::HandleType::FALLBACK;
         }
+
+        enum class AlgoType : uint32_t {
+            //! fallback
+            FB_NAIVE = 1 << 0,
+            FB_WINOGRAD_F32,
+            FB_WINOGRAD_4X4_F32,
+            FB_WINOGRAD_QS8,
+            FB_WINOGRAD_8X8_QS8,
+            FB_CONV1x1,
+            FB_CONV1x1_GEMV,
+            FB_IM2COL,
+
+#if MEGDNN_X86
+            X86_DIRECT = 1 << 8,
+            X86_DIRECT_STRD2,
+            X86_WINOGRAD_F63_8x8_F32,
+            X86_WINOGRAD_F23_8x8_F32,
+            X86_MKLDNN,
+            X86_CHANWISE_AVX2_STRD1_QINT8,
+            X86_CHANWISE_AVX2_STRD2_QINT8,
+            X86_DIRECT_AVX2_STRD1_INT8,
+            X86_DIRECT_AVX2_STRD2_INT8,
+            X86_MKLDNN_QINT8,
+            X86_MKLDNN_MATMUL_QINT8,
+#elif MEGDNN_AARCH64 || MEGDNN_ARMV7
+            ARM_COMMON_WINOGRAD_F23_FP16 = 1 << 8,
+            ARM_COMMON_WINOGRAD_F45_FP16,
+            ARM_COMMON_WINOGRAD_F63_FP16,
+            ARM_COMMON_WINOGRAD_F23_8X8_FP16,
+            ARM_COMMON_DIRECT_FP16,
+            ARM_COMMON_DIRECT_STRD1_FP16,
+            ARM_COMMON_WINOGRAD_F23_4X4_FP32,
+            ARM_COMMON_WINOGRAD_F63_FP32,
+            ARM_COMMON_WINOGRAD_F63_4X4_FP32,
+            ARM_COMMON_WINOGRAD_F54_FP32,
+            ARM_COMMON_WINOGRAD_F45_FP32,
+            ARM_COMMON_WINOGRAD_F23_4X4_NCHW44_F32,
+            ARM_COMMON_WINOGRAD_F63_4X4_NCHW44_F32,
+            ARM_COMMON_WINOGRAD_F73_4X4_NCHW44_F32,
+            ARM_COMMON_DIRECT_FP32,
+            ARM_COMMON_DIRECT_STRD1_FP32,
+            ARM_COMMON_DIRECT_STRD2_FP32,
+            ARM_COMMON_DIRECT_NCHW44_FP32,
+            ARM_COMMON_DIRECT_NCHW_NCHW44_FP32,
+            ARM_COMMON_CHWNWISE_NCHW44_F32,
+            ARM_COMMON_DIRECT_STRD1_S8,
+            ARM_COMMON_DIRECT_STRD2_S8,
+            ARM_COMMON_DIRECT_NCHW44,
+            ARM_COMMON_DIRECT_NCHW_NCHW44_S8,
+            ARM_COMMON_CHANWISE_STRD1_NCHW44_S8,
+            ARM_COMMON_CHANWISE_STRD2_NCHW44_S8,
+            ARM_COMMON_DIRECT_NCHW_NCHW44_DOT_S8,
+            ARM_COMMON_DIRECT_STRD1_DOT_S8,
+            ARM_COMMON_DIRECT_STRD2_DOT_S8,
+            ARM_COMMON_DIRECT_NCHW44_DOT_S8,
+            ARM_COMMON_WINOGRAD_F23_8X8_S8,
+            ARM_COMMON_WINOGRAD_F23_8X8_NCHW44_S8CF32,
+            ARM_COMMON_WINOGRAD_F23_8X8_NCHW44_S8,
+            ARM_COMMON_DIRECT_INT8X8X16,
+            ARM_COMMON_DIRECT_NCHW44_INT8X8X16,
+            ARM_COMMON_DIRECT_STRD2_INT8X8X16,
+            ARM_COMMON_DIRECT_STRD2_F2_INT8X8X16,
+            ARM_COMMON_CHWNWISE_STRD1_STRD2_NCHW44_INT8X8X16,
+            ARM_COMMON_DIRECT_NCHW_NCHW44_INT8X8X16,
+            ARM_COMMON_DIRECT_STRD1_QU8,
+            ARM_COMMON_DIRECT_STRD2_QU8,
+            ARM_COMMON_DIRECT_STRD1_DOT_QU8,
+            ARM_COMMON_DIRECT_STRD2_DOT_QU8,
+#if MEGDNN_AARCH64
+            AARCH64_DIRECT_STRD2_FP16,
+            AARCH64_DIRECT_STRD2_FP32,
+            AARCH64_MATMUL_S8,
+            AARCH64_MATMUL_QU8,
+#else
+            ARMV7_MATMUL_S8,
+            ARMV7_MATMUL_QU8,
+#endif // MEGDNN_AARCH64
+#endif
+        };
+
         virtual ~AlgoBase() = default;
         virtual bool usable(
                 const NCBKernSizeParam& param,
@@ -255,12 +335,14 @@ public:
 
         //! get the type of the algo
         virtual ConvAlgoTypePack get_algo_type() const = 0;
+        using Mapper = std::unordered_map<AlgorithmDesc, AlgoBase*>;
     };
 
+    using AlgoMapper = AlgoBase::Mapper;
     /**
      * \brief get all the algorithm for the opr.
      */
-    virtual SmallVector<AlgoBase*> algo_pack();
+    virtual SmallVector<AlgoBase*> get_all_packed_algo();
 
     /**
      * \brief select algo according to input algo type
@@ -305,6 +387,8 @@ private:
 
     bool is_naive_algo(ConvBiasImpl::Algorithm* algo);
 
+    Algorithm* get_algo_from_desc(const AlgorithmDesc& desc) const;
+
     //! get algorithm set by user or by heuristic
     Algorithm* get_algorithm(
             const NCBKernSizeParam& param,
@@ -320,6 +404,8 @@ private:
             _megdnn_tensor_in bias, _megdnn_tensor_out dst,
             _megdnn_workspace workspace,
             const PreprocessedFilter* preprocessed_filter);
+
+    static const AlgoPack& algo_pack();
 };
 
 inline bool is_enable_filter_preprocess(