refactor(mgb/dnn): replace reproducible with attribute

GitOrigin-RevId: d49015714c

dnn/include/megdnn/oprs/base.h

@@ -105,6 +105,10 @@ public:
      *
      */
     enum class Attribute : uint32_t {
+        /**
+         * \brief general algo.
+         */
+        DEFAULT = 0,
 
         /**
          * \brief whether the execution result is
@@ -163,6 +167,8 @@ public:
 
     bool contain_attribute(const Attribute& attr) const;
 
+    static std::string attribute_str(const Attribute& attr);
+
     Handle::HandleType handle_type() const { return m_handle_type; }
     Info info() const {
         return {{handle_type(), type(), param()}, name(), attribute()};
@@ -311,6 +317,7 @@ class MultiAlgoOpr<Opr, 3> : public MultiAlgoOpr<Opr, -1> {
 public:
     using Algorithm = detail::Algorithm;
     using AlgorithmInfo = detail::Algorithm::Info;
+    using AlgoAttribute = detail::Algorithm::Attribute;
 
     //! get all possible algorithm decriptions for the specified layouts
     std::vector<AlgorithmInfo> get_all_algorithms_info(const TensorLayout& p0,
@@ -335,9 +342,9 @@ public:
             const TensorLayout& p2,
             size_t workspace_limit_in_bytes =
                     std::numeric_limits<size_t>::max(),
-            bool reproducible = false) {
+            const AlgoAttribute& attr = AlgoAttribute::DEFAULT) {
         return get_algorithm_heuristic(p0, p1, p2, workspace_limit_in_bytes,
-                                       reproducible)
+                                       attr)
                 ->info();
     }
 
@@ -360,7 +367,7 @@ protected:
             const TensorLayout& p2,
             size_t workspace_limit_in_bytes =
                     std::numeric_limits<size_t>::max(),
-            bool reproducible = false) = 0;
+            const AlgoAttribute& attr = AlgoAttribute::DEFAULT) = 0;
 };
 
 //! specializae for nargs == 4
@@ -369,6 +376,7 @@ class MultiAlgoOpr<Opr, 4> : public MultiAlgoOpr<Opr, -1> {
 public:
     using Algorithm = detail::Algorithm;
     using AlgorithmInfo = detail::Algorithm::Info;
+    using AlgoAttribute = detail::Algorithm::Attribute;
 
     //! get all possible algorithm decriptions for the specified layouts
     std::vector<AlgorithmInfo> get_all_algorithms_info(const TensorLayout& p0,
@@ -394,9 +402,9 @@ public:
             const TensorLayout& p2, const TensorLayout& p3,
             size_t workspace_limit_in_bytes =
                     std::numeric_limits<size_t>::max(),
-            bool reproducible = false) {
+            const AlgoAttribute& attr = AlgoAttribute::DEFAULT) {
         return get_algorithm_heuristic(p0, p1, p2, p3, workspace_limit_in_bytes,
-                                       reproducible)
+                                       attr)
                 ->info();
     }
 
@@ -419,7 +427,7 @@ protected:
             const TensorLayout& p2, const TensorLayout& p3,
             size_t workspace_limit_in_bytes =
                     std::numeric_limits<size_t>::max(),
-            bool reproducible = false) = 0;
+            const AlgoAttribute& attr = AlgoAttribute::DEFAULT) = 0;
 };
 
 //! specializae for nargs == 5
@@ -428,6 +436,7 @@ class MultiAlgoOpr<Opr, 5> : public MultiAlgoOpr<Opr, -1> {
 public:
     using Algorithm = detail::Algorithm;
     using AlgorithmInfo = detail::Algorithm::Info;
+    using AlgoAttribute = detail::Algorithm::Attribute;
 
     //! get all possible algorithm decriptions for the specified layouts
     std::vector<AlgorithmInfo> get_all_algorithms_info(const TensorLayout& p0,
@@ -455,9 +464,9 @@ public:
             const TensorLayout& p4,
             size_t workspace_limit_in_bytes =
                     std::numeric_limits<size_t>::max(),
-            bool reproducible = false) {
+            const AlgoAttribute& attr = AlgoAttribute::DEFAULT) {
         return get_algorithm_heuristic(p0, p1, p2, p3, p4,
-                                       workspace_limit_in_bytes, reproducible)
+                                       workspace_limit_in_bytes, attr)
                 ->info();
     }
 
@@ -482,7 +491,7 @@ protected:
             const TensorLayout& p4,
             size_t workspace_limit_in_bytes =
                     std::numeric_limits<size_t>::max(),
-            bool reproducible = false) = 0;
+            const AlgoAttribute& attr = AlgoAttribute::DEFAULT) = 0;
 };
 
 //! specializae for nargs == 8
@@ -491,6 +500,7 @@ class MultiAlgoOpr<Opr, 8> : public MultiAlgoOpr<Opr, -1> {
 public:
     using Algorithm = detail::Algorithm;
     using AlgorithmInfo = detail::Algorithm::Info;
+    using AlgoAttribute = detail::Algorithm::Attribute;
 
     //! get all possible algorithm decriptions for the specified layouts
     std::vector<AlgorithmInfo> get_all_algorithms_info(
@@ -518,9 +528,9 @@ public:
             const TensorLayout& p6, const TensorLayout& p7,
             size_t workspace_limit_in_bytes =
                     std::numeric_limits<size_t>::max(),
-            bool reproducible = false) {
+            const AlgoAttribute& attr = AlgoAttribute::DEFAULT) {
         return get_algorithm_heuristic(p0, p1, p2, p3, p4, p5, p6, p7,
-                                       workspace_limit_in_bytes, reproducible)
+                                       workspace_limit_in_bytes, attr)
                 ->info();
     }
 
@@ -547,7 +557,7 @@ protected:
             const TensorLayout& p6, const TensorLayout& p7,
             size_t workspace_limit_in_bytes =
                     std::numeric_limits<size_t>::max(),
-            bool reproducible = false) = 0;
+            const AlgoAttribute& attr = AlgoAttribute::DEFAULT) = 0;
 };
 }  // namespace detail
 

dnn/src/common/algo_base.cpp

@@ -15,8 +15,39 @@
 
 using namespace megdnn;
 
+#define FOREACH_ALGO_ATTRIBUTE(cb) \
+    cb(DEFAULT)                    \
+    cb(REPRODUCIBLE)               \
+    cb(NAIVE)
+
+namespace {
+inline const char* attr_str(const AlgoAttribute& attr) {
+#define cb(attr)              \
+    case AlgoAttribute::attr: \
+        return #attr;
+    switch (attr) { FOREACH_ALGO_ATTRIBUTE(cb) }
+#undef cb
+    return "unknown arch";
+}
+}  // namespace
+
+std::string Algorithm::attribute_str(const Attribute& attr) {
+    std::string ret;
+    uint32_t attr_val = static_cast<uint32_t>(attr);
+    while(attr_val) {
+        uint32_t mask = ~(attr_val & (attr_val - 1));
+        Attribute sub_attr = static_cast<Attribute>(mask & attr_val);
+        if (!ret.empty()) {
+            ret.append(" | ");
+        }
+        ret.append(attr_str(sub_attr));
+        attr_val = attr_val & (attr_val - 1);
+    }
+    return ret;
+}
+
 bool Algorithm::contain_attribute(const Attribute& attr) const {
-    return bool(attribute() & attr);
+    return attr == static_cast<Attribute>(attribute() & attr);
 }
 
 // vim: syntax=cpp.doxygen

dnn/src/common/algo_chooser.h

@@ -32,7 +32,7 @@ typename Opr::AlgoBase* get_algorithm(Opr* opr, Args&&... args) {
     } else {
         ret = opr->get_algorithm_info_heuristic(
                 std::forward<Args>(args)..., std::numeric_limits<size_t>::max(),
-                false).desc;
+                AlgoAttribute::DEFAULT).desc;
     }
     return static_cast<typename Opr::AlgoBase*>(
             opr->get_algorithm_from_desc(ret));
@@ -51,7 +51,7 @@ typename Opr::AlgoBase* get_algorithm_or_construct(Opr* opr, Args&&... args) {
         return static_cast<typename Opr::AlgoBase*>(
                 opr->get_algorithm_heuristic(std::forward<Args>(args)...,
                                              std::numeric_limits<size_t>::max(),
-                                             false));
+                                             AlgoAttribute::DEFAULT));
     }
 }
 
@@ -74,37 +74,34 @@ std::vector<typename Opr::Algorithm*> get_all_algorithms(
 }
 
 /*!
- * \brief a helper function to get a reproducible algorithm. If require a
- * reproducible algorithm, and the given algorithm is reproducible, return the
- * given algorithm. Otherwise return nullptr
+ * \brief a helper function to get an algorithm with attribute. If require a
+ * algorithm with specified attribute, and the given algorithm has that
+ * attribute, return the given algorithm. Otherwise return nullptr
  */
 template <typename Opr>
-typename Opr::Algorithm* get_reproducible_algo(typename Opr::AlgoBase* algo,
-                                               bool reproducible) {
-    if (reproducible) {
-        if (algo->contain_attribute(AlgoAttribute::REPRODUCIBLE)) {
-            return algo;
-        }
-    } else {
+typename Opr::Algorithm* get_algo_with_attribute(typename Opr::AlgoBase* algo,
+                                               const AlgoAttribute& attr) {
+    if (algo->contain_attribute(attr)) {
         return algo;
     }
     return nullptr;
 }
 
 template <typename Opr>
-typename Opr::Algorithm* get_reproducible_algo(
+typename Opr::Algorithm* get_algo_with_attribute(
         const std::vector<typename Opr::AlgoBase*>& algos,
         const typename Opr::AlgoBase::SizeArgs& args,
-        size_t workspace_limit_in_bytes, const char* name) {
+        size_t workspace_limit_in_bytes, const char* name,
+        const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE) {
     size_t min_workspace_limit_in_bytes = std::numeric_limits<size_t>::max();
     bool available_but_limited_by_workspace = false;
-    bool available_but_not_reproducible = false;
+    bool available_but_without_attribute = false;
     for (auto i : algos) {
-        if (i->is_available_reproducible(args, true,
+        if (i->is_available_attribute(args, attr,
                                          workspace_limit_in_bytes)) {
             return i;
         }
-        if (i->is_available_reproducible(args)) {
+        if (i->is_available_attribute(args)) {
             if (i->get_workspace_in_bytes(args) > workspace_limit_in_bytes) {
                 available_but_limited_by_workspace = true;
                 min_workspace_limit_in_bytes =
@@ -113,20 +110,22 @@ typename Opr::Algorithm* get_reproducible_algo(
             }
         }
         if (i->is_available(args)) {
-            if (!i->contain_attribute(AlgoAttribute::REPRODUCIBLE))
-                available_but_not_reproducible = true;
+            if (!i->contain_attribute(attr))
+                available_but_without_attribute = true;
         }
     }
 
     MEGDNN_MARK_USED_VAR(name);
     if (available_but_limited_by_workspace) {
         megdnn_throw(ssprintf(
-                "no reproducible %s algorithm: %s workspace limit %zu is "
+                "no %s algorithm with attribute:%s : %s workspace limit %zu is "
                 "less than mini workspace limit %zu",
-                name, args.to_string().c_str(), workspace_limit_in_bytes,
+                name, Algorithm::attribute_str(attr).c_str(),
+                args.to_string().c_str(), workspace_limit_in_bytes,
                 min_workspace_limit_in_bytes));
-    } else if (available_but_not_reproducible) {
-        megdnn_throw(ssprintf("no reproducible %s algorithm", name));
+    } else if (available_but_without_attribute) {
+        megdnn_throw(ssprintf("no %s algorithm with attribute:%s", name,
+                               Algorithm::attribute_str(attr).c_str()));
     } else {
         megdnn_throw(ssprintf("no usable %s algorithm", name));
     }

dnn/src/cuda/batch_conv_bias/algo.h

@@ -65,12 +65,11 @@ public:
         return is_available(args) && get_workspace_in_bytes(args) <= limit;
     }
 
-    bool is_available_reproducible(
-            const SizeArgs& args, bool reproducible = true,
+    bool is_available_attribute(
+            const SizeArgs& args,
+            const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE,
             size_t limit = std::numeric_limits<size_t>::max()) {
-        return (!reproducible ||
-                contain_attribute(AlgoAttribute::REPRODUCIBLE)) &&
-               is_available_wk(args, limit);
+        return contain_attribute(attr) && is_available_wk(args, limit);
     }
 
     AlgoBase& check_workspace(const SizeArgs& args,

dnn/src/cuda/batch_conv_bias/opr_impl.cpp

@@ -22,21 +22,21 @@ BatchConvBiasForwardImpl::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) {
+        const AlgoAttribute& attr) {
     AlgoBase::SizeArgs args(this, src, filter, bias, z, dst);
-    if (sm_algo_pack.int8_nchw4_gemm_dotprod.is_available_reproducible(
-                args, reproducible, workspace_limit_in_bytes)) {
+    if (sm_algo_pack.int8_nchw4_gemm_dotprod.is_available_attribute(
+                args, attr, workspace_limit_in_bytes)) {
         return &sm_algo_pack.int8_nchw4_gemm_dotprod;
     }
-    if (sm_algo_pack.int8_nchw4_implicit_gemm_dotprod.is_available_reproducible(
-                args, reproducible, workspace_limit_in_bytes)) {
+    if (sm_algo_pack.int8_nchw4_implicit_gemm_dotprod.is_available_attribute(
+                args, attr, workspace_limit_in_bytes)) {
         return &sm_algo_pack.int8_nchw4_implicit_gemm_dotprod;
     }
-    megdnn_throw(
-            ssprintf("no %s batch conv bias algorithm with args(%s) and "
-                     "workspace limit (%zu bytes)",
-                     reproducible ? "reproducible" : "usable",
-                     args.to_string().c_str(), workspace_limit_in_bytes));
+    megdnn_throw(ssprintf(
+            "no batch conv bias algorithm with attribute%s args(%s) and "
+            "workspace limit (%zu bytes)",
+            Algorithm::attribute_str(attr).c_str(), args.to_string().c_str(),
+            workspace_limit_in_bytes));
 }
 
 std::vector<BatchConvBiasForwardImpl::Algorithm*>

dnn/src/cuda/batch_conv_bias/opr_impl.h

@@ -48,7 +48,7 @@ protected:
                                        const TensorLayout& z,
                                        const TensorLayout& dst,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
+                                       const AlgoAttribute& attr) override;
 
 private:
     static AlgoPack sm_algo_pack;

dnn/src/cuda/batched_matrix_mul/algo.h

@@ -68,12 +68,11 @@ public:
     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,
+    bool is_available_attribute(
+            const SizeArgs& args,
+            const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE,
             size_t limit = std::numeric_limits<size_t>::max()) {
-        return (!reproducible ||
-                contain_attribute(AlgoAttribute::REPRODUCIBLE)) &&
-               is_available_wk(args, limit);
+        return contain_attribute(attr) && is_available_wk(args, limit);
     }
     AlgoBase& check_workspace(const SizeArgs& args,
                               const Workspace& workspace) {

dnn/src/cuda/batched_matrix_mul/opr_impl.cpp

@@ -55,24 +55,21 @@ std::vector<Algorithm*> BatchedMatrixMulForwardImpl::get_all_algorithms(
 
 Algorithm* BatchedMatrixMulForwardImpl::get_algorithm_heuristic(
         const TensorLayout& A, const TensorLayout& B, const TensorLayout& C,
-        size_t workspace_limit_in_bytes, bool reproducible) {
+        size_t workspace_limit_in_bytes, const AlgoAttribute& attr) {
     MEGDNN_MARK_USED_VAR(workspace_limit_in_bytes);
     AlgoBase::SizeArgs args(this, A, B, C);
-    if (sm_algo_pack.cublas.is_available_reproducible(args, reproducible)) {
+    if (sm_algo_pack.cublas.is_available_attribute(args, attr)) {
         return &sm_algo_pack.cublas;
     }
 #if CUDA_VERSION >= 10010
-    else if (sm_algo_pack.cublasLt.is_available_reproducible(args,
-                                                             reproducible)) {
+    else if (sm_algo_pack.cublasLt.is_available_attribute(args, attr)) {
         return &sm_algo_pack.cublasLt;
     }
 #endif
-    else if (sm_algo_pack.int8x8x32.is_available_reproducible(args,
-                                                              reproducible)) {
+    else if (sm_algo_pack.int8x8x32.is_available_attribute(args, attr)) {
         return &sm_algo_pack.int8x8x32;
     } else {
-        if (sm_algo_pack.brute_force.is_available_reproducible(args,
-                                                               reproducible)) {
+        if (sm_algo_pack.brute_force.is_available_attribute(args, attr)) {
             return &sm_algo_pack.brute_force;
         }
     }

dnn/src/cuda/batched_matrix_mul/opr_impl.h

@@ -49,7 +49,7 @@ protected:
                                        const TensorLayout& B,
                                        const TensorLayout& C,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
+                                       const AlgoAttribute& attr) override;
 
 private:
     static AlgoPack sm_algo_pack;

dnn/src/cuda/conv_bias/algo.h

@@ -127,12 +127,11 @@ public:
         return is_available(args) && get_workspace_in_bytes(args) <= limit;
     }
 
-    bool is_available_reproducible(
-            const SizeArgs& args, bool reproducible = true,
+    bool is_available_attribute(
+            const SizeArgs& args,
+            const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE,
             size_t limit = std::numeric_limits<size_t>::max()) {
-        return (!reproducible ||
-                contain_attribute(AlgoAttribute::REPRODUCIBLE)) &&
-               is_available_wk(args, limit);
+        return contain_attribute(attr) && is_available_wk(args, limit);
     }
 
     AlgoBase& check_workspace(const SizeArgs& args,

dnn/src/cuda/conv_bias/opr_impl.cpp

@@ -51,7 +51,7 @@ ConvBiasForward::Algorithm* ConvBiasForwardImpl::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) {
+        const AlgoAttribute& attr) {
     using namespace conv_bias;
     AlgoBase::SizeArgs args{this, src, filter, bias, z, dst};
     auto dst_layout = *args.dst_layout;
@@ -74,7 +74,7 @@ ConvBiasForward::Algorithm* ConvBiasForwardImpl::get_algorithm_heuristic(
     };
 
     auto get_cudnn_algo =
-            [this, &conv_args, &args, workspace_limit_in_bytes, reproducible](
+            [this, &conv_args, &args, workspace_limit_in_bytes, attr](
                     const thin_function<AlgoBase*(cudnnConvolutionFwdAlgo_t)>&
                             cb) -> AlgoBase* {
         auto cudnn_handle = cuda::cudnn_handle(this->handle());
@@ -92,8 +92,8 @@ ConvBiasForward::Algorithm* ConvBiasForwardImpl::get_algorithm_heuristic(
                 &ret_count, algo_perf.data()));
         for (int i = 0; i < ret_count; ++i) {
             auto conv_bias_algo = cb(algo_perf[i].algo);
-            if (conv_bias_algo->is_available_reproducible(
-                        args, reproducible, workspace_limit_in_bytes))
+            if (conv_bias_algo->is_available_attribute(
+                        args, attr, workspace_limit_in_bytes))
                 return conv_bias_algo;
         }
 #else
@@ -105,18 +105,18 @@ ConvBiasForward::Algorithm* ConvBiasForwardImpl::get_algorithm_heuristic(
                 workspace_limit_in_bytes, &algo));
 
         auto conv_bias_algo = cb(algo);
-        if (conv_bias_algo->is_available_reproducible(args, reproducible,
-                                                      workspace_limit_in_bytes))
+        if (conv_bias_algo->is_available_attribute(args, attr,
+                                                   workspace_limit_in_bytes))
             return conv_bias_algo;
 #endif
         return nullptr;
     };
 
     auto get_1x1_algo = [workspace_limit_in_bytes,
-                         reproducible](const AlgoBase::SizeArgs& size_arg)
+                         attr](const AlgoBase::SizeArgs& size_arg)
             -> ConvBiasForwardImpl::AlgoBase* {
-        if (sm_algo_pack.batched_matmul.is_available_reproducible(
-                    size_arg, reproducible, workspace_limit_in_bytes)) {
+        if (sm_algo_pack.batched_matmul.is_available_attribute(
+                    size_arg, attr, workspace_limit_in_bytes)) {
             return &sm_algo_pack.batched_matmul;
         }
         return nullptr;
@@ -144,11 +144,11 @@ ConvBiasForward::Algorithm* ConvBiasForwardImpl::get_algorithm_heuristic(
     //! avoid bad case in cudnn, check dnn chanwise impl first
     if (is_chanwise) {
         if (prefer_dnn_chanwise) {
-            if (sm_algo_pack.chanwise.is_available_reproducible(
-                        args, reproducible, workspace_limit_in_bytes))
+            if (sm_algo_pack.chanwise.is_available_attribute(
+                        args, attr, workspace_limit_in_bytes))
                 return &sm_algo_pack.chanwise;
-            if (sm_algo_pack.chanwise8x8x32.is_available_reproducible(
-                        args, reproducible, workspace_limit_in_bytes))
+            if (sm_algo_pack.chanwise8x8x32.is_available_attribute(
+                        args, attr, workspace_limit_in_bytes))
                 return &sm_algo_pack.chanwise8x8x32;
         } else {
             conv_args.dst_layout = &dst_layout;
@@ -163,8 +163,7 @@ ConvBiasForward::Algorithm* ConvBiasForwardImpl::get_algorithm_heuristic(
     //! Prefer CUDNN CONVBIAS.
     bool cudnn_conv_bias_act_supported = false;
     for (auto&& algo : sm_algo_pack.cudnn_conv_bias_activations) {
-        if (algo.is_available_reproducible(args, reproducible,
-                                           workspace_limit_in_bytes)) {
+        if (algo.is_available_attribute(args, attr, workspace_limit_in_bytes)) {
             cudnn_conv_bias_act_supported = true;
             break;
         }
@@ -201,26 +200,26 @@ ConvBiasForward::Algorithm* ConvBiasForwardImpl::get_algorithm_heuristic(
         return algo;
     }
 
-    if (sm_algo_pack.fallback_nchw_qs8.is_available_reproducible(
-                args, reproducible, workspace_limit_in_bytes)) {
+    if (sm_algo_pack.fallback_nchw_qs8.is_available_attribute(
+                args, attr, workspace_limit_in_bytes)) {
         return &sm_algo_pack.fallback_nchw_qs8;
     }
 
     if (args.src_layout->dtype.enumv() != DTypeTrait<dtype::BFloat16>::enumv) {
-        if (reproducible) {
-            return megdnn::get_reproducible_algo<ConvBiasForwardImpl>(
+        if (attr != AlgoAttribute::DEFAULT) {
+            return megdnn::get_algo_with_attribute<ConvBiasForwardImpl>(
                     sm_algo_pack.non_cudnn_algos, args,
-                    workspace_limit_in_bytes, "cuda convbias fwd");
+                    workspace_limit_in_bytes, "cuda convbias fwd", attr);
         } else {
             return megdnn::get_usable_algo<ConvBiasForwardImpl>(
                     sm_algo_pack.non_cudnn_algos, args,
                     workspace_limit_in_bytes, "cuda convbias fwd");
         }
     } else {
-        if (reproducible) {
-            return megdnn::get_reproducible_algo<ConvBiasForwardImpl>(
+        if (attr != AlgoAttribute::DEFAULT) {
+            return megdnn::get_algo_with_attribute<ConvBiasForwardImpl>(
                     sm_algo_pack.bfloat16_algos, args, workspace_limit_in_bytes,
-                    "cuda convbias fwd");
+                    "cuda convbias fwd", attr);
         } else {
             return megdnn::get_usable_algo<ConvBiasForwardImpl>(
                     sm_algo_pack.bfloat16_algos, args, workspace_limit_in_bytes,

dnn/src/cuda/conv_bias/opr_impl.h

@@ -82,7 +82,7 @@ public:
                                        const TensorLayout& z,
                                        const TensorLayout& dst,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
+                                       const AlgoAttribute& attr) override;
 
 private:
     static AlgoPack sm_algo_pack;

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

@@ -82,12 +82,11 @@ public:
         return is_available(args) && get_workspace_in_bytes(args) <= limit;
     }
 
-    bool is_available_reproducible(
-            const SizeArgs& args, bool reproducible = true,
+    bool is_available_attribute(
+            const SizeArgs& args,
+            const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE,
             size_t limit = std::numeric_limits<size_t>::max()) {
-        return (!reproducible ||
-                contain_attribute(AlgoAttribute::REPRODUCIBLE)) &&
-               is_available_wk(args, limit);
+        return contain_attribute(attr) && is_available_wk(args, limit);
     }
 
     AlgoBase& check_workspace(const SizeArgs& args,

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

@@ -78,12 +78,11 @@ public:
         return is_available(args) && get_workspace_in_bytes(args) <= limit;
     }
 
-    bool is_available_reproducible(
-            const SizeArgs& args, bool reproducible = true,
+    bool is_available_attribute(
+            const SizeArgs& args,
+            const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE,
             size_t limit = std::numeric_limits<size_t>::max()) {
-        return (!reproducible ||
-                contain_attribute(AlgoAttribute::REPRODUCIBLE)) &&
-               is_available_wk(args, limit);
+        return contain_attribute(attr) && is_available_wk(args, limit);
     }
 
     AlgoBase& check_workspace(const SizeArgs& args,

dnn/src/cuda/convolution/forward/algos.h

@@ -63,13 +63,13 @@ public:
     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()) const {
-        return (!reproducible ||
-                contain_attribute(AlgoAttribute::REPRODUCIBLE)) &&
-               is_available_wk(args, limit);
+    bool is_available_attribute(
+            const SizeArgs& args,
+            const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE,
+            size_t limit = std::numeric_limits<size_t>::max()) {
+        return contain_attribute(attr) && is_available_wk(args, limit);
     }
+
     AlgoBase& check_workspace(const SizeArgs& args,
                               const Workspace& workspace) {
         auto req = get_workspace_in_bytes(args);

dnn/src/cuda/convolution/opr_impl.cpp

@@ -12,6 +12,7 @@
 
 #include "src/cuda/convolution/opr_impl.h"
 #include "megdnn/dtype.h"
+#include "src/common/algo_chooser.h"
 #include "src/cuda/convolution/helper.h"
 #include "src/cuda/convolution/forward/algos.h"
 #include "src/cuda/convolution/backward_data/algo.h"
@@ -36,10 +37,10 @@ ConvolutionForwardImpl::get_algorithm_heuristic(const TensorLayout& src,
                                                 const TensorLayout& filter,
                                                 const TensorLayout& dst,
                                                 size_t workspace_limit_in_bytes,
-                                                bool reproducible) {
+                                                const AlgoAttribute& attr) {
     AlgoBase::SizeArgs args{this, src, filter, dst};
     MEGDNN_MARK_USED_VAR(workspace_limit_in_bytes);
-    MEGDNN_MARK_USED_VAR(reproducible);
+    MEGDNN_MARK_USED_VAR(attr);
     return &sm_algo_pack.algo_default;
 }
 
@@ -100,32 +101,32 @@ ConvolutionBackwardDataImpl::Algorithm*
 ConvolutionBackwardDataImpl::get_algorithm_heuristic(
         const TensorLayout& filter, const TensorLayout& diff,
         const TensorLayout& grad, size_t workspace_limit_in_bytes,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     auto fm = check_layout_fwd(grad, filter, diff);
     return get_algorithm_heuristic(filter, fm, diff, grad,
-                                   workspace_limit_in_bytes, reproducible);
+                                   workspace_limit_in_bytes, attr);
 }
 
 ConvolutionBackwardDataImpl::Algorithm*
-ConvolutionBackwardDataImpl::get_algorithm_heuristic(
-        const TensorLayout& filter, const CanonizedFilterMeta& filter_meta,
-        const TensorLayout& diff, const TensorLayout& grad,
-        size_t workspace_limit_in_bytes, bool reproducible) {
+ConvolutionBackwardDataImpl::get_algorithm_heuristic(const TensorLayout& filter,
+        const CanonizedFilterMeta& filter_meta, const TensorLayout& diff,
+        const TensorLayout& grad, size_t workspace_limit_in_bytes,
+        const AlgoAttribute& attr) {
     AlgoBase::SizeArgs args(this, filter, filter_meta, diff, grad);
 
     if (args.filter_meta.group > 1 &&
-        sm_algo_pack.chanwise.is_available_reproducible(
-                args, reproducible, workspace_limit_in_bytes)) {
+        sm_algo_pack.chanwise.is_available_attribute(
+                args, attr, workspace_limit_in_bytes)) {
         // prefer special chanwise impl
         return &sm_algo_pack.chanwise;
     }
 
     if (args.filter_layout->dtype.enumv() ==
         DTypeTrait<dtype::QuantizedS8>::enumv) {
-        if (reproducible) {
-            return megdnn::get_reproducible_algo<ConvolutionBackwardDataImpl>(
+        if (attr != AlgoAttribute::DEFAULT) {
+            return megdnn::get_algo_with_attribute<ConvolutionBackwardDataImpl>(
                     sm_algo_pack.int8_algos, args, workspace_limit_in_bytes,
-                    "cuda conv bwd_data");
+                    "cuda conv bwd_data", attr);
         } else {
             return megdnn::get_usable_algo<ConvolutionBackwardDataImpl>(
                     sm_algo_pack.int8_algos, args, workspace_limit_in_bytes,
@@ -133,9 +134,8 @@ ConvolutionBackwardDataImpl::get_algorithm_heuristic(
         }
     }
 
-    auto get_cudnn_algo =
-            [this, &args, workspace_limit_in_bytes,
-             reproducible]() -> ConvolutionBackwardDataImpl::AlgoBase* {
+    auto get_cudnn_algo = [this, &args, workspace_limit_in_bytes,
+                           attr]() -> ConvolutionBackwardDataImpl::AlgoBase* {
         auto cudnn_handle = cuda::cudnn_handle(this->handle());
         CUDNNBwdDataDescs desc;
         args.init_desc(desc);
@@ -153,7 +153,7 @@ ConvolutionBackwardDataImpl::get_algorithm_heuristic(
         for (int i = 0; i < ret_count; ++i) {
             if (algo_perf[i].memory > workspace_limit_in_bytes)
                 continue;
-            if (reproducible) {
+            if (attr & AlgoAttribute::REPRODUCIBLE) {
                 if (algo_perf[i].determinism == CUDNN_DETERMINISTIC) {
                     return reinterpret_cast<AlgoBase*>(
                             sm_algo_pack.cudnn_from_enum(algo_perf[i].algo));
@@ -174,8 +174,8 @@ ConvolutionBackwardDataImpl::get_algorithm_heuristic(
         auto&& cast_algo =
                 reinterpret_cast<AlgoBase*>(sm_algo_pack.cudnn_from_enum(algo));
         return reinterpret_cast<AlgoBase*>(
-                megdnn::get_reproducible_algo<ConvolutionBackwardDataImpl>(
-                        cast_algo, reproducible));
+                megdnn::get_algo_with_attribute<ConvolutionBackwardDataImpl>(
+                        cast_algo, attr));
 #endif
     };
 
@@ -197,20 +197,20 @@ ConvolutionBackwardDataImpl::get_algorithm_heuristic(
 
     if (args.filter_layout->dtype.enumv() !=
         DTypeTrait<dtype::BFloat16>::enumv) {
-        if (reproducible) {
-            return megdnn::get_reproducible_algo<ConvolutionBackwardDataImpl>(
+        if (attr != AlgoAttribute::DEFAULT) {
+            return megdnn::get_algo_with_attribute<ConvolutionBackwardDataImpl>(
                     sm_algo_pack.non_cudnn_algos, args,
-                    workspace_limit_in_bytes, "cuda conv bwd_data");
+                    workspace_limit_in_bytes, "cuda conv bwd_data", attr);
         } else {
             return megdnn::get_usable_algo<ConvolutionBackwardDataImpl>(
                     sm_algo_pack.non_cudnn_algos, args,
                     workspace_limit_in_bytes, "cuda conv bwd_data");
         }
     } else {
-        if (reproducible) {
-            return megdnn::get_reproducible_algo<ConvolutionBackwardDataImpl>(
+        if (attr != AlgoAttribute::DEFAULT) {
+            return megdnn::get_algo_with_attribute<ConvolutionBackwardDataImpl>(
                     sm_algo_pack.bfloat16_algos, args, workspace_limit_in_bytes,
-                    "cuda conv bwd_data");
+                    "cuda conv bwd_data", attr);
         } else {
             return megdnn::get_usable_algo<ConvolutionBackwardDataImpl>(
                     sm_algo_pack.bfloat16_algos, args, workspace_limit_in_bytes,
@@ -255,29 +255,29 @@ ConvolutionBackwardFilterImpl::Algorithm*
 ConvolutionBackwardFilterImpl::get_algorithm_heuristic(
         const TensorLayout& src, const TensorLayout& diff,
         const TensorLayout& grad, size_t workspace_limit_in_bytes,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     auto fm = check_layout_fwd(src, grad, diff);
     return get_algorithm_heuristic(src, diff, grad, fm,
-                                   workspace_limit_in_bytes, reproducible);
+                                   workspace_limit_in_bytes, attr);
 }
 
 ConvolutionBackwardFilterImpl::Algorithm*
 ConvolutionBackwardFilterImpl::get_algorithm_heuristic(
         const TensorLayout& src, const TensorLayout& diff,
         const TensorLayout& grad, const CanonizedFilterMeta& grad_meta,
-        size_t workspace_limit_in_bytes, bool reproducible) {
+        size_t workspace_limit_in_bytes, const AlgoAttribute& attr) {
     AlgoBase::SizeArgs args(this, src, diff, grad, grad_meta);
 
     if (args.grad_filter_meta.group > 1 &&
-        sm_algo_pack.chanwise.is_available_reproducible(
-                args, reproducible, workspace_limit_in_bytes)) {
+        sm_algo_pack.chanwise.is_available_attribute(
+                args, attr, workspace_limit_in_bytes)) {
         // prefer special chanwise impl
         return &sm_algo_pack.chanwise;
     }
 
     auto get_cudnn_algo =
             [this, &args, workspace_limit_in_bytes,
-             reproducible]() -> ConvolutionBackwardFilterImpl::AlgoBase* {
+             attr]() -> ConvolutionBackwardFilterImpl::AlgoBase* {
         auto cudnn_handle = cuda::cudnn_handle(this->handle());
         CUDNNBwdFilterDescs desc;
         args.init_desc(desc);
@@ -305,7 +305,7 @@ ConvolutionBackwardFilterImpl::get_algorithm_heuristic(
         for (int i = 0; i < ret_count; ++i) {
             if (algo_perf[i].memory > workspace_limit_in_bytes)
                 continue;
-            if (reproducible) {
+            if (attr & AlgoAttribute::REPRODUCIBLE) {
                 if (algo_perf[i].determinism == CUDNN_DETERMINISTIC) {
                     return reinterpret_cast<AlgoBase*>(
                             sm_algo_pack.cudnn_from_enum(algo_perf[i].algo));
@@ -326,8 +326,8 @@ ConvolutionBackwardFilterImpl::get_algorithm_heuristic(
         auto&& cast_algo =
                 reinterpret_cast<AlgoBase*>(sm_algo_pack.cudnn_from_enum(algo));
         return reinterpret_cast<AlgoBase*>(
-                megdnn::get_reproducible_algo<ConvolutionBackwardFilterImpl>(
-                        cast_algo, reproducible));
+                megdnn::get_algo_with_attribute<ConvolutionBackwardFilterImpl>(
+                        cast_algo, attr));
 #endif
     };
 
@@ -348,20 +348,22 @@ ConvolutionBackwardFilterImpl::get_algorithm_heuristic(
     }
 
     if (args.src_layout->dtype.enumv() != DTypeTrait<dtype::BFloat16>::enumv) {
-        if (reproducible) {
-            return megdnn::get_reproducible_algo<ConvolutionBackwardFilterImpl>(
+        if (attr != AlgoAttribute::DEFAULT) {
+            return megdnn::get_algo_with_attribute<
+                    ConvolutionBackwardFilterImpl>(
                     sm_algo_pack.non_cudnn_algos, args,
-                    workspace_limit_in_bytes, "cuda conv bwd_filter");
+                    workspace_limit_in_bytes, "cuda conv bwd_filter", attr);
         } else {
             return megdnn::get_usable_algo<ConvolutionBackwardFilterImpl>(
                     sm_algo_pack.non_cudnn_algos, args,
                     workspace_limit_in_bytes, "cuda conv bwd_filter");
         }
     } else {
-        if (reproducible) {
-            return megdnn::get_reproducible_algo<ConvolutionBackwardFilterImpl>(
+        if (attr != AlgoAttribute::DEFAULT) {
+            return megdnn::get_algo_with_attribute<
+                    ConvolutionBackwardFilterImpl>(
                     sm_algo_pack.bfloat16_algos, args, workspace_limit_in_bytes,
-                    "cuda conv bwd_filter");
+                    "cuda conv bwd_filter", attr);
         } else {
             return megdnn::get_usable_algo<ConvolutionBackwardFilterImpl>(
                     sm_algo_pack.bfloat16_algos, args, workspace_limit_in_bytes,

dnn/src/cuda/convolution/opr_impl.h

@@ -63,7 +63,7 @@ protected:
                                        const TensorLayout& filter,
                                        const TensorLayout& dst,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
+                                       const AlgoAttribute& attr) override;
 
 private:
     static AlgoPack sm_algo_pack;
@@ -77,9 +77,9 @@ public:
     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) {
+            size_t workspace_limit_in_bytes, const AlgoAttribute& attr) {
         return get_algorithm_heuristic(filter, filter_meta, diff, grad,
-                                       workspace_limit_in_bytes, reproducible)
+                                       workspace_limit_in_bytes, attr)
                 ->info();
     }
 
@@ -87,9 +87,9 @@ public:
                                                const TensorLayout& diff,
                                                const TensorLayout& grad,
                                                size_t workspace_limit_in_bytes,
-                                               bool reproducible) {
+                                               const AlgoAttribute& attr) {
         return get_algorithm_heuristic(filter, diff, grad,
-                                       workspace_limit_in_bytes, reproducible)
+                                       workspace_limit_in_bytes, attr)
                 ->info();
     }
 
@@ -122,7 +122,7 @@ protected:
                                        const TensorLayout& diff,
                                        const TensorLayout& grad,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
+                                       const AlgoAttribute& attr) override;
 
 private:
     Algorithm* get_algorithm_heuristic(const TensorLayout& filter,
@@ -130,7 +130,7 @@ private:
                                        const TensorLayout& diff,
                                        const TensorLayout& grad,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible);
+                                       const AlgoAttribute& attr);
 
     static AlgoPack sm_algo_pack;
 };
@@ -146,9 +146,9 @@ public:
     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) {
+            size_t workspace_limit_in_bytes, const AlgoAttribute& attr) {
         return get_algorithm_heuristic(src, diff, grad, grad_meta,
-                                       workspace_limit_in_bytes, reproducible)
+                                       workspace_limit_in_bytes, attr)
                 ->info();
     }
 
@@ -156,9 +156,9 @@ public:
                                                const TensorLayout& diff,
                                                const TensorLayout& grad,
                                                size_t workspace_limit_in_bytes,
-                                               bool reproducible) {
+                                               const AlgoAttribute& attr) {
         return get_algorithm_heuristic(filter, diff, grad,
-                                       workspace_limit_in_bytes, reproducible)
+                                       workspace_limit_in_bytes, attr)
                 ->info();
     }
 
@@ -185,7 +185,7 @@ protected:
                                        const TensorLayout& diff,
                                        const TensorLayout& grad,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
+                                       const AlgoAttribute& attr) override;
 
 private:
     Algorithm* get_algorithm_heuristic(const TensorLayout& src,
@@ -193,7 +193,7 @@ private:
                                        const TensorLayout& grad,
                                        const CanonizedFilterMeta& grad_meta,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible);
+                                       const AlgoAttribute& attr);
 
     static AlgoPack sm_algo_pack;
 };

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

@@ -75,12 +75,11 @@ public:
     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,
+    bool is_available_attribute(
+            const SizeArgs& args,
+            const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE,
             size_t limit = std::numeric_limits<size_t>::max()) {
-        return (!reproducible ||
-                contain_attribute(AlgoAttribute::REPRODUCIBLE)) &&
-               is_available_wk(args, limit);
+        return contain_attribute(attr) && is_available_wk(args, limit);
     }
     AlgoBase& check_workspace(const SizeArgs& args,
                               const Workspace& workspace) {

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

@@ -69,12 +69,11 @@ public:
     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,
+    bool is_available_attribute(
+            const SizeArgs& args,
+            const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE,
             size_t limit = std::numeric_limits<size_t>::max()) {
-        return (!reproducible ||
-                contain_attribute(AlgoAttribute::REPRODUCIBLE)) &&
-               is_available_wk(args, limit);
+        return contain_attribute(attr) && is_available_wk(args, limit);
     }
     AlgoBase& check_workspace(const SizeArgs& args,
                               const Workspace& workspace) {

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

@@ -74,12 +74,11 @@ public:
     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,
+    bool is_available_attribute(
+            const SizeArgs& args,
+            const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE,
             size_t limit = std::numeric_limits<size_t>::max()) {
-        return (!reproducible ||
-                contain_attribute(AlgoAttribute::REPRODUCIBLE)) &&
-               is_available_wk(args, limit);
+        return contain_attribute(attr) && is_available_wk(args, limit);
     }
     AlgoBase& check_workspace(const SizeArgs& args,
                               const Workspace& workspace) {

dnn/src/cuda/convolution3d/helper.h

@@ -97,8 +97,8 @@ namespace convolution3d {
             const cudnnConvolutionDescriptor_t conv_desc,
             const cudnnTensorDescriptor_t y_desc,
             size_t workspace_limit_in_bytes, cudnnConvolutionFwdAlgo_t* algo,
-            bool reproducible) {
-        MEGDNN_MARK_USED_VAR(reproducible);
+            const AlgoAttribute& attr) {
+        MEGDNN_MARK_USED_VAR(attr);
 #if CUDNN_MAJOR >= 7
         int algo_max_count = 0;
         cudnn_check(cudnnGetConvolutionForwardAlgorithmMaxCount(
@@ -118,7 +118,7 @@ namespace convolution3d {
                     cudnn_handle, x_desc, w_desc, conv_desc, y_desc,
                     algo_perf[i].algo, &workspace_size));
             if (workspace_size > workspace_limit_in_bytes) continue;
-            if (!reproducible) {
+            if (!(attr & AlgoAttribute::REPRODUCIBLE)) {
                 *algo = algo_perf[i].algo;
                 return true;
             } else {
@@ -144,8 +144,8 @@ namespace convolution3d {
             const cudnnConvolutionDescriptor_t conv_desc,
             const cudnnTensorDescriptor_t dx_desc,
             size_t workspace_limit_in_bytes,
-            cudnnConvolutionBwdDataAlgo_t* algo, bool reproducible) {
-        MEGDNN_MARK_USED_VAR(reproducible);
+            cudnnConvolutionBwdDataAlgo_t* algo, const AlgoAttribute& attr) {
+        MEGDNN_MARK_USED_VAR(attr);
 #if CUDNN_MAJOR >= 7
         int algo_max_count = 0;
         cudnn_check(cudnnGetConvolutionBackwardDataAlgorithmMaxCount(
@@ -166,7 +166,7 @@ namespace convolution3d {
                     cudnn_handle, w_desc, dy_desc, conv_desc, dx_desc,
                     algo_perf[i].algo, &workspace_size));
             if (workspace_size > workspace_limit_in_bytes) continue;
-            if (!reproducible) {
+            if (!(attr & AlgoAttribute::REPRODUCIBLE)) {
                 *algo = algo_perf[i].algo;
                 return true;
             } else {
@@ -193,8 +193,8 @@ namespace convolution3d {
             const cudnnConvolutionDescriptor_t conv_desc,
             const cudnnFilterDescriptor_t dw_desc,
             size_t workspace_limit_in_bytes,
-            cudnnConvolutionBwdFilterAlgo_t* algo, bool reproducible) {
-        MEGDNN_MARK_USED_VAR(reproducible);
+            cudnnConvolutionBwdFilterAlgo_t* algo, const AlgoAttribute& attr) {
+        MEGDNN_MARK_USED_VAR(attr);
 #if CUDNN_MAJOR >= 7
         int algo_max_count = 0;
         cudnn_check(cudnnGetConvolutionBackwardFilterAlgorithmMaxCount(
@@ -207,14 +207,15 @@ namespace convolution3d {
                 algo_max_count, &algo_count, algo_perf.data()));
         for (int i = 0; i < algo_count; ++i) {
             if (algo_perf[i].algo ==
-                    cudnnConvolutionBwdFilterAlgo_t::CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT_TILING)
+                cudnnConvolutionBwdFilterAlgo_t::
+                        CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT_TILING)
                 continue;
             size_t workspace_size = 0;
             cudnn_check(cudnnGetConvolutionBackwardFilterWorkspaceSize(
                     cudnn_handle, x_desc, dy_desc, conv_desc, dw_desc,
                     algo_perf[i].algo, &workspace_size));
             if (workspace_size > workspace_limit_in_bytes) continue;
-            if (!reproducible) {
+            if (!(attr & AlgoAttribute::REPRODUCIBLE)) {
                 *algo = algo_perf[i].algo;
                 return true;
             } else {

dnn/src/cuda/convolution3d/opr_impl.cpp

@@ -15,6 +15,7 @@
 #include "./forward/algo.h"
 #include "./helper.h"
 
+#include "src/common/algo_chooser.h"
 #include "src/cuda/utils.h"
 
 using namespace megdnn;
@@ -32,16 +33,16 @@ Convolution3DForwardImpl::Algorithm*
 Convolution3DForwardImpl::get_algorithm_heuristic(
         const TensorLayout& src, const TensorLayout& filter,
         const TensorLayout& dst, size_t workspace_limit_in_bytes,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     auto fm = check_layout_fwd(src, filter, dst);
     return get_algorithm_heuristic(src, fm, dst, workspace_limit_in_bytes,
-                                   reproducible);
+                                   attr);
 }
 Convolution3DForwardImpl::Algorithm*
 Convolution3DForwardImpl::get_algorithm_heuristic(
         const TensorLayout& src, const CanonizedFilterMeta& filter,
         const TensorLayout& dst, size_t workspace_limit_in_bytes,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     AlgoBase::SizeArgs args(this, src, filter, dst);
 
 #if CUDNN_MAJOR < 7 || (CUDNN_MAJOR == 7 && CUDNN_MINOR < 5)
@@ -49,26 +50,26 @@ Convolution3DForwardImpl::get_algorithm_heuristic(
         // prefer special chanwise impl since as the group conv of cudnn whose
         // version is lower than v7.5.0 is still slower than our implementation
         // in many channel-wise cases
-        if (sm_algo_pack.chanwise.is_available_reproducible(
-                    args, reproducible, workspace_limit_in_bytes)) {
+        if (sm_algo_pack.chanwise.is_available_attribute(
+                    args, attr, workspace_limit_in_bytes)) {
             return &sm_algo_pack.chanwise;
         }
     }
 #endif
 
-    auto prefer_1x1x1 = [&args, reproducible, workspace_limit_in_bytes]() {
+    auto prefer_1x1x1 = [&args, attr, workspace_limit_in_bytes]() {
         const size_t MAX_BATCH_SIZE_FOR_1x1x1_MAT_ALGO = 4;
         size_t batch_size = args.src_layout->shape[0];
         if (batch_size > MAX_BATCH_SIZE_FOR_1x1x1_MAT_ALGO) {
             return false;
         }
-        return sm_algo_pack.a1x1x1.is_available_reproducible(
-                args, reproducible, workspace_limit_in_bytes);
+        return sm_algo_pack.a1x1x1.is_available_attribute(
+                args, attr, workspace_limit_in_bytes);
     };
 
     auto get_cudnn_algo =
             [this, &args, workspace_limit_in_bytes,
-             reproducible]() -> Convolution3DForwardImpl::AlgoBase* {
+             attr]() -> Convolution3DForwardImpl::AlgoBase* {
         auto cudnn_handle = cuda::cudnn_handle(this->handle());
         cudnnConvolutionFwdAlgo_t algo;
         CUDNNForwardDescs desc;
@@ -77,11 +78,11 @@ Convolution3DForwardImpl::get_algorithm_heuristic(
         bool got = cudnn_get_convolution_fwd_algo_helper(
                 cudnn_handle, desc.src_desc.desc, desc.filter_desc.desc,
                 desc.conv_desc.desc, desc.dst_desc.desc,
-                workspace_limit_in_bytes, &algo, reproducible);
+                workspace_limit_in_bytes, &algo, attr);
         if (got) {
             return static_cast<AlgoBase*>(
-                    megdnn::get_reproducible_algo<Convolution3DForwardImpl>(
-                            sm_algo_pack.cudnn_from_enum(algo), reproducible));
+                    megdnn::get_algo_with_attribute<Convolution3DForwardImpl>(
+                            sm_algo_pack.cudnn_from_enum(algo), attr));
         } else {
             return nullptr;
         }
@@ -107,10 +108,10 @@ Convolution3DForwardImpl::get_algorithm_heuristic(
         args = orig_args;
     }
 
-    if (reproducible) {
-        return megdnn::get_reproducible_algo<Convolution3DForwardImpl>(
+    if (attr != AlgoAttribute::DEFAULT) {
+        return megdnn::get_algo_with_attribute<Convolution3DForwardImpl>(
                 sm_algo_pack.non_cudnn_algos, args, workspace_limit_in_bytes,
-                "cuda conv3d fwd");
+                "cuda conv3d fwd", attr);
     } else {
         return megdnn::get_usable_algo<Convolution3DForwardImpl>(
                 sm_algo_pack.non_cudnn_algos, args, workspace_limit_in_bytes,
@@ -168,28 +169,28 @@ Convolution3DBackwardDataImpl::Algorithm*
 Convolution3DBackwardDataImpl::get_algorithm_heuristic(
         const TensorLayout& filter, const TensorLayout& diff,
         const TensorLayout& grad, size_t workspace_limit_in_bytes,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     auto fm = check_layout_fwd(grad, filter, diff);
     return get_algorithm_heuristic(fm, diff, grad, workspace_limit_in_bytes,
-                                   reproducible);
+                                   attr);
 }
 
 Convolution3DBackwardDataImpl::Algorithm*
 Convolution3DBackwardDataImpl::get_algorithm_heuristic(
         const CanonizedFilterMeta& filter, const TensorLayout& diff,
         const TensorLayout& grad, size_t workspace_limit_in_bytes,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     AlgoBase::SizeArgs args(this, filter, diff, grad);
 
     if (args.filter_meta.group > 1 &&
-        sm_algo_pack.chanwise.is_available_reproducible(
-                args, reproducible, workspace_limit_in_bytes)) {
+        sm_algo_pack.chanwise.is_available_attribute(
+                args, attr, workspace_limit_in_bytes)) {
         return &sm_algo_pack.chanwise;
     }
 
     auto get_cudnn_algo =
             [this, &args, workspace_limit_in_bytes,
-             reproducible]() -> Convolution3DBackwardDataImpl::AlgoBase* {
+             attr]() -> Convolution3DBackwardDataImpl::AlgoBase* {
         auto cudnn_handle = cuda::cudnn_handle(this->handle());
         cudnnConvolutionBwdDataAlgo_t algo;
         CUDNNBwdDataDescs desc;
@@ -197,11 +198,11 @@ Convolution3DBackwardDataImpl::get_algorithm_heuristic(
         bool got = cudnn_get_convolution_bwd_data_algo_helper(
                 cudnn_handle, desc.filter_desc.desc, desc.diff_desc.desc,
                 desc.conv_desc.desc, desc.grad_desc.desc,
-                workspace_limit_in_bytes, &algo, reproducible);
+                workspace_limit_in_bytes, &algo, attr);
         if (got) {
-            return static_cast<AlgoBase*>(megdnn::get_reproducible_algo<
+            return static_cast<AlgoBase*>(megdnn::get_algo_with_attribute<
                                           Convolution3DBackwardDataImpl>(
-                    sm_algo_pack.cudnn_from_enum(algo), reproducible));
+                    sm_algo_pack.cudnn_from_enum(algo), attr));
         } else {
             return nullptr;
         }
@@ -223,10 +224,10 @@ Convolution3DBackwardDataImpl::get_algorithm_heuristic(
         args = orig_args;
     }
 
-    if (reproducible) {
-        return megdnn::get_reproducible_algo<Convolution3DBackwardDataImpl>(
+    if (attr != AlgoAttribute::DEFAULT) {
+        return megdnn::get_algo_with_attribute<Convolution3DBackwardDataImpl>(
                 sm_algo_pack.non_cudnn_algos, args, workspace_limit_in_bytes,
-                "cuda conv3d bwd data");
+                "cuda conv3d bwd data", attr);
     } else {
         return megdnn::get_usable_algo<Convolution3DBackwardDataImpl>(
                 sm_algo_pack.non_cudnn_algos, args, workspace_limit_in_bytes,
@@ -268,28 +269,28 @@ Convolution3DBackwardFilterImpl::Algorithm*
 Convolution3DBackwardFilterImpl::get_algorithm_heuristic(
         const TensorLayout& src, const TensorLayout& diff,
         const TensorLayout& grad, size_t workspace_limit_in_bytes,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     auto fm = check_layout_fwd(src, grad, diff);
     return get_algorithm_heuristic(src, diff, fm, workspace_limit_in_bytes,
-                                   reproducible);
+                                   attr);
 }
 
 Convolution3DBackwardFilterImpl::Algorithm*
 Convolution3DBackwardFilterImpl::get_algorithm_heuristic(
         const TensorLayout& src, const TensorLayout& diff,
         const CanonizedFilterMeta& grad, size_t workspace_limit_in_bytes,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     AlgoBase::SizeArgs args(this, src, diff, grad);
 
     if (args.grad_filter_meta.group > 1 &&
-        sm_algo_pack.chanwise.is_available_reproducible(
-                args, reproducible, workspace_limit_in_bytes)) {
+        sm_algo_pack.chanwise.is_available_attribute(
+                args, attr, workspace_limit_in_bytes)) {
         return &sm_algo_pack.chanwise;
     }
 
     auto get_cudnn_algo =
             [this, &args, workspace_limit_in_bytes,
-             reproducible]() -> Convolution3DBackwardFilterImpl::AlgoBase* {
+             attr]() -> Convolution3DBackwardFilterImpl::AlgoBase* {
         auto cudnn_handle = cuda::cudnn_handle(this->handle());
         cudnnConvolutionBwdFilterAlgo_t algo;
         CUDNNBwdFilterDescs desc;
@@ -297,11 +298,11 @@ Convolution3DBackwardFilterImpl::get_algorithm_heuristic(
         bool got = cudnn_get_convolution_bwd_filter_algo_helper(
                 cudnn_handle, desc.src_desc.desc, desc.diff_desc.desc,
                 desc.conv_desc.desc, desc.grad_desc.desc,
-                workspace_limit_in_bytes, &algo, reproducible);
+                workspace_limit_in_bytes, &algo, attr);
         if (got) {
-            return static_cast<AlgoBase*>(megdnn::get_reproducible_algo<
+            return static_cast<AlgoBase*>(megdnn::get_algo_with_attribute<
                                           Convolution3DBackwardFilterImpl>(
-                    sm_algo_pack.cudnn_from_enum(algo), reproducible));
+                    sm_algo_pack.cudnn_from_enum(algo), attr));
         } else {
             return nullptr;
         }
@@ -322,10 +323,10 @@ Convolution3DBackwardFilterImpl::get_algorithm_heuristic(
         args = orig_args;
     }
 
-    if (reproducible) {
-        return megdnn::get_reproducible_algo<Convolution3DBackwardFilterImpl>(
+    if (attr != AlgoAttribute::DEFAULT) {
+        return megdnn::get_algo_with_attribute<Convolution3DBackwardFilterImpl>(
                 sm_algo_pack.non_cudnn_algos, args, workspace_limit_in_bytes,
-                "cuda conv3d bwd filter");
+                "cuda conv3d bwd filter", attr);
     } else {
         return megdnn::get_usable_algo<Convolution3DBackwardFilterImpl>(
                 sm_algo_pack.non_cudnn_algos, args, workspace_limit_in_bytes,

dnn/src/cuda/convolution3d/opr_impl.h

@@ -25,9 +25,9 @@ public:
                                        const CanonizedFilterMeta& filter,
                                        const TensorLayout& dst,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) {
+                                       const AlgoAttribute& attr) {
         return get_algorithm_heuristic(src, filter, dst,
-                                       workspace_limit_in_bytes, reproducible)
+                                       workspace_limit_in_bytes, attr)
                 ->info();
     }
     size_t get_workspace_in_bytes(const TensorLayout& src,
@@ -52,14 +52,14 @@ protected:
                                        const TensorLayout& filter,
                                        const TensorLayout& dst,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
+                                       const AlgoAttribute& attr) override;
 
 private:
     Algorithm* get_algorithm_heuristic(const TensorLayout& src,
                                        const CanonizedFilterMeta& filter,
                                        const TensorLayout& dst,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible);
+                                       const AlgoAttribute& attr);
 
 
     static AlgoPack sm_algo_pack;
@@ -73,9 +73,9 @@ public:
     AlgorithmInfo get_algorithm_info_heuristic(
             const CanonizedFilterMeta& filter, const TensorLayout& diff,
             const TensorLayout& grad, size_t workspace_limit_in_bytes,
-            bool reproducible) {
+            const AlgoAttribute& attr) {
         return get_algorithm_heuristic(filter, diff, grad,
-                                       workspace_limit_in_bytes, reproducible)
+                                       workspace_limit_in_bytes, attr)
                 ->info();
     }
     size_t get_workspace_in_bytes(const TensorLayout& filter,
@@ -102,14 +102,14 @@ protected:
                                        const TensorLayout& diff,
                                        const TensorLayout& grad,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
+                                       const AlgoAttribute& attr) override;
 
 private:
     Algorithm* get_algorithm_heuristic(const CanonizedFilterMeta& filter,
                                        const TensorLayout& diff,
                                        const TensorLayout& grad,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible);
+                                       const AlgoAttribute& attr);
 
     static AlgoPack sm_algo_pack;
 };
@@ -126,9 +126,9 @@ public:
                                                const TensorLayout& diff,
                                                const CanonizedFilterMeta& grad,
                                                size_t workspace_limit_in_bytes,
-                                               bool reproducible) {
+                                               const AlgoAttribute& attr) {
         return get_algorithm_heuristic(src, diff, grad,
-                                       workspace_limit_in_bytes, reproducible)
+                                       workspace_limit_in_bytes, attr)
                 ->info();
     }
 
@@ -153,14 +153,14 @@ protected:
                                        const TensorLayout& diff,
                                        const TensorLayout& grad,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
+                                       const AlgoAttribute& attr) override;
 
 private:
     Algorithm* get_algorithm_heuristic(const TensorLayout& src,
                                        const TensorLayout& diff,
                                        const CanonizedFilterMeta& grad,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible);
+                                       const AlgoAttribute& attr);
 
     static AlgoPack sm_algo_pack;
 };

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

@@ -80,12 +80,11 @@ public:
     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,
+    bool is_available_attribute(
+            const SizeArgs& args,
+            const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE,
             size_t limit = std::numeric_limits<size_t>::max()) {
-        return (!reproducible ||
-                contain_attribute(AlgoAttribute::REPRODUCIBLE)) &&
-               is_available_wk(args, limit);
+        return contain_attribute(attr) && is_available_wk(args, limit);
     }
     AlgoBase& check_workspace(const SizeArgs& args,
                               const Workspace& workspace) {

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

@@ -73,12 +73,11 @@ public:
     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,
+    bool is_available_attribute(
+            const SizeArgs& args,
+            const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE,
             size_t limit = std::numeric_limits<size_t>::max()) {
-        return (!reproducible ||
-                contain_attribute(AlgoAttribute::REPRODUCIBLE)) &&
-               is_available_wk(args, limit);
+        return contain_attribute(attr) && is_available_wk(args, limit);
     }
     AlgoBase& check_workspace(const SizeArgs& args,
                               const Workspace& workspace) {

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

@@ -68,12 +68,11 @@ public:
     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,
+    bool is_available_attribute(
+            const SizeArgs& args,
+            const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE,
             size_t limit = std::numeric_limits<size_t>::max()) {
-        return (!reproducible ||
-                contain_attribute(AlgoAttribute::REPRODUCIBLE)) &&
-               is_available_wk(args, limit);
+        return contain_attribute(attr) && is_available_wk(args, limit);
     }
     AlgoBase& check_workspace(const SizeArgs& args,
                               const Workspace& workspace) {

dnn/src/cuda/deformable_conv/opr_impl.cpp

@@ -59,10 +59,10 @@ AlgoFwd* Fwd::get_algorithm_heuristic(const TensorLayout& im,
                                       const TensorLayout& mask,
                                       const TensorLayout& dst,
                                       size_t workspace_limit_in_bytes,
-                                      bool reproducible) {
+                                      const AlgoAttribute& attr) {
     auto fm = make_canonized_filter_meta(im.ndim, filter, offset);
     return get_algorithm_heuristic(im, fm, offset, mask, dst,
-                                   workspace_limit_in_bytes, reproducible);
+                                   workspace_limit_in_bytes, attr);
 }
 
 AlgoFwd* Fwd::get_algorithm_heuristic(const TensorLayout& im,
@@ -71,17 +71,17 @@ AlgoFwd* Fwd::get_algorithm_heuristic(const TensorLayout& im,
                                       const TensorLayout& mask,
                                       const TensorLayout& dst,
                                       size_t workspace_limit_in_bytes,
-                                      bool reproducible) {
+                                      const AlgoAttribute& attr) {
     AlgoBase::SizeArgs args(this, im, filter, offset, mask, dst);
-    if (sm_algo_pack.algo_matmul.is_available_reproducible(
-                args, reproducible, workspace_limit_in_bytes)) {
+    if (sm_algo_pack.algo_matmul.is_available_attribute(
+                args, attr, workspace_limit_in_bytes)) {
         return &sm_algo_pack.algo_matmul;
     }
-    megdnn_throw(
-            ssprintf("no %s deformable conv fwd algorithm with args(%s) and "
-                     "workspace limit (%zu bytes)",
-                     reproducible ? "reproducible" : "usable",
-                     args.to_string().c_str(), workspace_limit_in_bytes));
+    megdnn_throw(ssprintf(
+            "no deformable conv fwd algorithm with attribute%s , args(%s) and "
+            "workspace limit (%zu bytes)",
+            Algorithm::attribute_str(attr).c_str(), args.to_string().c_str(),
+            workspace_limit_in_bytes));
 }
 
 const char* Fwd::get_algorithm_set_name() const {
@@ -115,27 +115,28 @@ AlgoBwdFlt* BwdFlt::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) {
+        size_t workspace_limit_in_bytes, const AlgoAttribute& attr) {
     auto fm = make_canonized_filter_meta(im.ndim, filter_grad, offset);
     return get_algorithm_heuristic(im, offset, mask, out_grad, fm,
-                                   workspace_limit_in_bytes, reproducible);
+                                   workspace_limit_in_bytes, attr);
 }
 
 AlgoBwdFlt* BwdFlt::get_algorithm_heuristic(
         const TensorLayout& im, const TensorLayout& offset,
         const TensorLayout& mask, const TensorLayout& out_grad,
         const CanonizedFilterMeta& filter_grad,
-        size_t workspace_limit_in_bytes, bool reproducible) {
+        size_t workspace_limit_in_bytes, const AlgoAttribute& attr) {
     AlgoBase::SizeArgs args(this, im, offset, mask, out_grad, filter_grad);
-    if (sm_algo_pack.algo_matmul.is_available_reproducible(
-                args, reproducible, workspace_limit_in_bytes)) {
+    if (sm_algo_pack.algo_matmul.is_available_attribute(
+                args, attr, workspace_limit_in_bytes)) {
         return &sm_algo_pack.algo_matmul;
     }
-    megdnn_throw(ssprintf(
-            "no %s deformable conv bwd filter algorithm with args(%s) and "
-            "workspace limit (%zu bytes)",
-            reproducible ? "reproducible" : "usable", args.to_string().c_str(),
-            workspace_limit_in_bytes));
+    megdnn_throw(
+            ssprintf("no deformable conv bwd filter algorithm with "
+                     "attribute%s, args(%s) and "
+                     "workspace limit (%zu bytes)",
+                     Algorithm::attribute_str(attr).c_str(),
+                     args.to_string().c_str(), workspace_limit_in_bytes));
 }
 
 size_t BwdFlt::get_workspace_in_bytes(
@@ -175,11 +176,11 @@ AlgoBwdData* BwdData::get_algorithm_heuristic(
         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) {
+        size_t workspace_limit_in_bytes, const AlgoAttribute& attr) {
     auto fm = make_canonized_filter_meta(im.ndim, filter, offset);
     return get_algorithm_heuristic(im, fm, offset, mask, out_grad, im_grad,
                                    offset_grad, mask_grad,
-                                   workspace_limit_in_bytes, reproducible);
+                                   workspace_limit_in_bytes, attr);
 }
 
 AlgoBwdData* BwdData::get_algorithm_heuristic(
@@ -187,18 +188,19 @@ AlgoBwdData* BwdData::get_algorithm_heuristic(
         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) {
+        size_t workspace_limit_in_bytes, const AlgoAttribute& attr) {
     AlgoBase::SizeArgs args(this, im, filter, offset, mask, out_grad, im_grad,
                             offset_grad, mask_grad);
-    if (sm_algo_pack.algo_matmul.is_available_reproducible(
-                args, reproducible, workspace_limit_in_bytes)) {
+    if (sm_algo_pack.algo_matmul.is_available_attribute(
+                args, attr, workspace_limit_in_bytes)) {
         return &sm_algo_pack.algo_matmul;
     }
-    megdnn_throw(ssprintf(
-            "no %s deformable conv bwd data algorithm with args(%s) and "
-            "workspace limit (%zu bytes)",
-            reproducible ? "reproducible" : "usable", args.to_string().c_str(),
-            workspace_limit_in_bytes));
+    megdnn_throw(
+            ssprintf("no deformable conv bwd data algorithm with attribute%s, "
+                     "args(%s) and "
+                     "workspace limit (%zu bytes)",
+                     Algorithm::attribute_str(attr).c_str(),
+                     args.to_string().c_str(), workspace_limit_in_bytes));
 }
 
 size_t BwdData::get_workspace_in_bytes(

dnn/src/cuda/deformable_conv/opr_impl.h

@@ -36,7 +36,7 @@ public:
                                        const TensorLayout& mask,
                                        const TensorLayout& dst,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible);
+                                       const AlgoAttribute& attr);
 
     const char* get_algorithm_set_name() const override;
 
@@ -60,7 +60,7 @@ protected:
                                        const TensorLayout& mask,
                                        const TensorLayout& dst,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
+                                       const AlgoAttribute& attr) override;
 
 private:
     static AlgoPack sm_algo_pack;
@@ -81,7 +81,7 @@ public:
                                        const TensorLayout& out_grad,
                                        const CanonizedFilterMeta& filter_grad,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible);
+                                       const AlgoAttribute& attr);
 
     size_t get_workspace_in_bytes(const TensorLayout& im,
                                   const TensorLayout& offset,
@@ -111,7 +111,7 @@ protected:
                                        const TensorLayout& out_grad,
                                        const TensorLayout& filter_grad,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
+                                       const AlgoAttribute& attr) override;
 
 private:
     static AlgoPack sm_algo_pack;
@@ -132,7 +132,7 @@ public:
             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);
+            size_t workspace_limit_in_bytes, const AlgoAttribute& attr);
 
     size_t get_workspace_in_bytes(const TensorLayout& im,
                                   const TensorLayout& filter,
@@ -166,7 +166,8 @@ protected:
             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;
+            size_t workspace_limit_in_bytes,
+            const AlgoAttribute& attr) override;
 
 private:
     static AlgoPack sm_algo_pack;

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

@@ -59,12 +59,11 @@ public:
     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,
+    bool is_available_attribute(
+            const SizeArgs& args,
+            const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE,
             size_t limit = std::numeric_limits<size_t>::max()) {
-        return (!reproducible ||
-                contain_attribute(AlgoAttribute::REPRODUCIBLE)) &&
-               is_available_wk(args, limit);
+        return contain_attribute(attr) && is_available_wk(args, limit);
     }
     AlgoBase& check_workspace(const SizeArgs& args,
                               const Workspace& workspace) {

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

@@ -59,12 +59,11 @@ public:
     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,
+    bool is_available_attribute(
+            const SizeArgs& args,
+            const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE,
             size_t limit = std::numeric_limits<size_t>::max()) {
-        return (!reproducible ||
-                contain_attribute(AlgoAttribute::REPRODUCIBLE)) &&
-               is_available_wk(args, limit);
+        return contain_attribute(attr) && is_available_wk(args, limit);
     }
     AlgoBase& check_workspace(const SizeArgs& args,
                               const Workspace& workspace) {

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

@@ -60,12 +60,11 @@ public:
     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,
+    bool is_available_attribute(
+            const SizeArgs& args,
+            const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE,
             size_t limit = std::numeric_limits<size_t>::max()) {
-        return (!reproducible ||
-                contain_attribute(AlgoAttribute::REPRODUCIBLE)) &&
-               is_available_wk(args, limit);
+        return contain_attribute(attr) && is_available_wk(args, limit);
     }
     AlgoBase& check_workspace(const SizeArgs& args,
                               const Workspace& workspace) {

dnn/src/cuda/local_share/opr_impl.cpp

@@ -24,26 +24,26 @@ LocalShareForwardImpl::get_algorithm_heuristic(const TensorLayout& src,
                                                const TensorLayout& filter,
                                                const TensorLayout& dst,
                                                size_t workspace_limit_in_bytes,
-                                               bool reproducible) {
+                                               const AlgoAttribute& attr) {
     AlgoBase::SizeArgs args(this, src, filter, dst);
     if (sm_algo_pack.batch_size_aware_chwn_small_image
-                .is_available_reproducible(args, reproducible,
+                .is_available_attribute(args, attr,
                                            workspace_limit_in_bytes)) {
         return &sm_algo_pack.batch_size_aware_chwn_small_image;
     }
-    if (sm_algo_pack.batch_size_aware_chwn.is_available_reproducible(
-                args, reproducible, workspace_limit_in_bytes)) {
+    if (sm_algo_pack.batch_size_aware_chwn.is_available_attribute(
+                args, attr, workspace_limit_in_bytes)) {
         return &sm_algo_pack.batch_size_aware_chwn;
     }
-    if (sm_algo_pack.batched_matmul.is_available_reproducible(
-                args, reproducible, workspace_limit_in_bytes)) {
+    if (sm_algo_pack.batched_matmul.is_available_attribute(
+                args, attr, workspace_limit_in_bytes)) {
         return &sm_algo_pack.batched_matmul;
     }
-    megdnn_throw(
-            ssprintf("no %s local share conv algorithm with args(%s) and "
-                     "workspace limit (%zu bytes)",
-                     reproducible ? "reproducible" : "usable",
-                     args.to_string().c_str(), workspace_limit_in_bytes));
+    megdnn_throw(ssprintf(
+            "no local share conv algorithm with attribute%s, args(%s) and "
+            "workspace limit (%zu bytes)",
+            Algorithm::attribute_str(attr).c_str(), args.to_string().c_str(),
+            workspace_limit_in_bytes));
 }
 
 std::vector<LocalShareForwardImpl::Algorithm*>
@@ -79,21 +79,21 @@ LocalShareBackwardDataImpl::Algorithm*
 LocalShareBackwardDataImpl::get_algorithm_heuristic(
         const TensorLayout& filter, const TensorLayout& diff,
         const TensorLayout& grad, size_t workspace_limit_in_bytes,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     AlgoBase::SizeArgs args(this, filter, diff, grad);
-    if (sm_algo_pack.implicit_gemm.is_available_reproducible(
-                args, reproducible, workspace_limit_in_bytes)) {
+    if (sm_algo_pack.implicit_gemm.is_available_attribute(
+                args, attr, workspace_limit_in_bytes)) {
         return &sm_algo_pack.implicit_gemm;
     }
-    if (sm_algo_pack.batched_matmul.is_available_reproducible(
-                args, reproducible, workspace_limit_in_bytes)) {
+    if (sm_algo_pack.batched_matmul.is_available_attribute(
+                args, attr, workspace_limit_in_bytes)) {
         return &sm_algo_pack.batched_matmul;
     }
-    megdnn_throw(
-            ssprintf("no %s local share bwd data algorithm with args(%s) and "
-                     "workspace limit (%zu bytes)",
-                     reproducible ? "reproducible" : "usable",
-                     args.to_string().c_str(), workspace_limit_in_bytes));
+    megdnn_throw(ssprintf(
+            "no local share bwd data algorithm with attribute%s args(%s) and "
+            "workspace limit (%zu bytes)",
+            Algorithm::attribute_str(attr).c_str(), args.to_string().c_str(),
+            workspace_limit_in_bytes));
 }
 
 std::vector<LocalShareBackwardDataImpl::Algorithm*>
@@ -129,20 +129,21 @@ LocalShareBackwardFilterImpl::Algorithm*
 LocalShareBackwardFilterImpl::get_algorithm_heuristic(
         const TensorLayout& src, const TensorLayout& diff,
         const TensorLayout& grad, size_t workspace_limit_in_bytes,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     AlgoBase::SizeArgs args(this, src, diff, grad);
-    if (sm_algo_pack.implicit_gemm.is_available_reproducible(
-                args, reproducible, workspace_limit_in_bytes)) {
+    if (sm_algo_pack.implicit_gemm.is_available_attribute(
+                args, attr, workspace_limit_in_bytes)) {
         return &sm_algo_pack.implicit_gemm;
     }
-    if (sm_algo_pack.batched_matmul.is_available_reproducible(
-                args, reproducible, workspace_limit_in_bytes)) {
+    if (sm_algo_pack.batched_matmul.is_available_attribute(
+                args, attr, workspace_limit_in_bytes)) {
         return &sm_algo_pack.batched_matmul;
     }
     megdnn_throw(
-            ssprintf("no %s local share bwd filter algorithm with args(%s) and "
+            ssprintf("no local share bwd filter algorithm with attribute%s, "
+                     "args(%s) and "
                      "workspace limit (%zu bytes)",
-                     reproducible ? "reproducible" : "usable",
+                     Algorithm::attribute_str(attr).c_str(),
                      args.to_string().c_str(), workspace_limit_in_bytes));
 }
 

dnn/src/cuda/local_share/opr_impl.h

@@ -43,7 +43,7 @@ protected:
                                        const TensorLayout& filter,
                                        const TensorLayout& dst,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
+                                       const AlgoAttribute& attr) override;
 private:
     static AlgoPack sm_algo_pack;
 };
@@ -75,7 +75,7 @@ protected:
                                        const TensorLayout& diff,
                                        const TensorLayout& grad,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
+                                       const AlgoAttribute& attr) override;
 
 private:
     static AlgoPack sm_algo_pack;
@@ -108,7 +108,7 @@ protected:
                                        const TensorLayout& diff,
                                        const TensorLayout& grad,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
+                                       const AlgoAttribute& attr) override;
 
 private:
     static AlgoPack sm_algo_pack;

dnn/src/cuda/matrix_mul/algos.h

@@ -83,12 +83,11 @@ public:
     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,
+    bool is_available_attribute(
+            const SizeArgs& args,
+            const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE,
             size_t limit = std::numeric_limits<size_t>::max()) const {
-        return (!reproducible ||
-                contain_attribute(AlgoAttribute::REPRODUCIBLE)) &&
-               is_available_wk(args, limit);
+        return contain_attribute(attr) && is_available_wk(args, limit);
     }
     AlgoBase& check_workspace(const SizeArgs& args,
                               const Workspace& workspace) {

dnn/src/cuda/matrix_mul/opr_impl.cpp

@@ -30,30 +30,30 @@ MatrixMulForwardImpl::get_all_algorithms(const TensorLayout& A,
 
 MatrixMulForwardImpl::Algorithm* MatrixMulForwardImpl::get_algorithm_heuristic(
         const TensorLayout& A, const TensorLayout& B, const TensorLayout& C,
-        size_t workspace_limit_in_bytes, bool reproducible) {
+        size_t workspace_limit_in_bytes, const AlgoAttribute& attr) {
     AlgoBase::SizeArgs args{this, A, B, C};
-    if (sm_algo_pack.cublas.is_available_reproducible(
-                args, reproducible, workspace_limit_in_bytes)) {
+    if (sm_algo_pack.cublas.is_available_attribute(args, attr,
+                                                   workspace_limit_in_bytes)) {
         return &sm_algo_pack.cublas;
     }
 #if CUDA_VERSION >= 10010
-    if (sm_algo_pack.cublas_lt.is_available_reproducible(
-                args, reproducible, workspace_limit_in_bytes)) {
+    if (sm_algo_pack.cublas_lt.is_available_attribute(
+                args, attr, workspace_limit_in_bytes)) {
         return &sm_algo_pack.cublas_lt;
     }
 #endif
 
 #if CUDA_VERSION >= 10000
-    if (sm_algo_pack.wmma_uint4x4x32.is_available_reproducible(
-                args, reproducible, workspace_limit_in_bytes)) {
+    if (sm_algo_pack.wmma_uint4x4x32.is_available_attribute(
+                args, attr, workspace_limit_in_bytes)) {
         return &sm_algo_pack.wmma_uint4x4x32;
     }
 #endif
 
-    if (reproducible) {
-        return megdnn::get_reproducible_algo<MatrixMulForwardImpl>(
+    if (attr != AlgoAttribute::DEFAULT) {
+        return megdnn::get_algo_with_attribute<MatrixMulForwardImpl>(
                 sm_algo_pack.all_algos, args, workspace_limit_in_bytes,
-                "matrix mul forward");
+                "matrix mul forward", attr);
     } else {
         return megdnn::get_usable_algo<MatrixMulForwardImpl>(
                 sm_algo_pack.all_algos, args, workspace_limit_in_bytes,

dnn/src/cuda/matrix_mul/opr_impl.h

@@ -61,7 +61,7 @@ protected:
                                        const TensorLayout& B,
                                        const TensorLayout& C,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
+                                       const AlgoAttribute& attr) override;
 
 private:
     static AlgoPack sm_algo_pack;

dnn/src/fallback/batched_matrix_mul/algos.h

@@ -63,12 +63,11 @@ public:
     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,
+    bool is_available_attribute(
+            const SizeArgs& args,
+            const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE,
             size_t limit = std::numeric_limits<size_t>::max()) const {
-        return (!reproducible ||
-                contain_attribute(AlgoAttribute::REPRODUCIBLE)) &&
-               is_available_wk(args, limit);
+        return contain_attribute(attr) && is_available_wk(args, limit);
     }
     AlgoBase& check_workspace(const SizeArgs& args,
                               const Workspace& workspace) {

dnn/src/fallback/batched_matrix_mul/opr_impl.cpp

@@ -31,16 +31,16 @@ BatchedMatrixMulForwardImpl::get_all_algorithms(const TensorLayout& A,
 BatchedMatrixMulForwardImpl::Algorithm*
 BatchedMatrixMulForwardImpl::get_algorithm_heuristic(
         const TensorLayout& A, const TensorLayout& B, const TensorLayout& C,
-        size_t workspace_limit_in_bytes, bool reproducible) {
+        size_t workspace_limit_in_bytes, const AlgoAttribute& attr) {
     AlgoBase::SizeArgs args{this, A, B, C};
-    if (sm_algo_pack.algo_default.is_available_reproducible(
-                args, reproducible, workspace_limit_in_bytes)) {
+    if (sm_algo_pack.algo_default.is_available_attribute(
+                args, attr, workspace_limit_in_bytes)) {
         return &sm_algo_pack.algo_default;
     }
-    if (reproducible) {
-        return megdnn::get_reproducible_algo<BatchedMatrixMulForwardImpl>(
+    if (attr != AlgoAttribute::DEFAULT) {
+        return megdnn::get_algo_with_attribute<BatchedMatrixMulForwardImpl>(
                 sm_algo_pack.all_algos, args, workspace_limit_in_bytes,
-                "batched matrix mul forward");
+                "batched matrix mul forward", attr);
     } else {
         return megdnn::get_usable_algo<BatchedMatrixMulForwardImpl>(
                 sm_algo_pack.all_algos, args, workspace_limit_in_bytes,

dnn/src/fallback/batched_matrix_mul/opr_impl.h

@@ -40,7 +40,7 @@ private:
                                        const TensorLayout& /*B*/,
                                        const TensorLayout& /*C*/,
                                        size_t /*workspace_limit_in_bytes*/,
-                                       bool /*reproducible*/) override;
+                                       const AlgoAttribute& /*attr*/) override;
 
     const char* get_algorithm_set_name() const override {
         return "FALLBACK BATCHED MATMUL";

dnn/src/fallback/conv_bias/opr_impl.cpp

@@ -280,32 +280,29 @@ ConvBiasImpl::Algorithm* ConvBiasImpl::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) {
+        const AlgoAttribute& attr) {
     auto fparam = make_ncb_kern_size_param(src, filter, bias, dst, nullptr);
     auto result = get_algorithm_heuristic_with_ncb(
-            fparam, workspace_limit_in_bytes, reproducible);
+            fparam, workspace_limit_in_bytes, attr);
     if (result == nullptr) {
         result = naive::ConvBiasForwardImpl::get_algorithm_heuristic(
-                src, filter, bias, z, dst, workspace_limit_in_bytes,
-                reproducible);
+                src, filter, bias, z, dst, workspace_limit_in_bytes, attr);
     }
     return result;
 }
 
 ConvBiasImpl::Algorithm* ConvBiasImpl::get_algorithm_heuristic_with_ncb(
         const NCBKernSizeParam& param, size_t workspace_limit_in_bytes,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     auto algo_data_type = param.deduce_algo_data_type();
     auto suggest_category_order = suggest_algo_category_order(param);
     for (auto category : suggest_category_order) {
         auto&& origin_algos = select_algo_type({algo_data_type, category});
         ConvBiasImpl::Algorithm* heuristic_algo = nullptr;
         for (auto i : origin_algos) {
-            bool usable_reproducible =
-                    static_cast<AlgoBase*>(i)->usable_reproducible(
-                            param, AlgoSelectionStrategy::HEURISTIC,
-                            reproducible);
-            if (usable_reproducible &&
+            bool usable_attribute = static_cast<AlgoBase*>(i)->usable_attribute(
+                    param, AlgoSelectionStrategy::HEURISTIC, attr);
+            if (usable_attribute &&
                 static_cast<AlgoBase*>(i)->get_workspace(param) <=
                         workspace_limit_in_bytes) {
                 //! store the first usable algo if no prefer algo, choose it as
@@ -499,8 +496,8 @@ ConvBiasImpl::Algorithm* ConvBiasImpl::get_algorithm(
     }
     if (!m_prev_selected_algo ||
         memcmp(&m_prev_selected_algo_sizep, &param, sizeof(NCBKernSizeParam))) {
-        m_prev_selected_algo =
-                get_algorithm_heuristic_with_ncb(param, workspace_size);
+        m_prev_selected_algo = get_algorithm_heuristic_with_ncb(
+                param, workspace_size, AlgoAttribute::DEFAULT);
         m_prev_selected_algo_sizep = param;
     }
     return m_prev_selected_algo;

dnn/src/fallback/conv_bias/opr_impl.h

@@ -95,9 +95,7 @@ public:
                                        const TensorLayout& z,
                                        const TensorLayout& dst,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
-
-
+                                       const AlgoAttribute& attr) override;
 
     //! size param for kernels with non-contiguous batch
     struct NCBKernSizeParam : ConvolutionImpl::NCBKernSizeParam {
@@ -321,11 +319,11 @@ public:
             return false;
         }
 
-        bool usable_reproducible(const NCBKernSizeParam& param,
-                                 AlgoSelectionStrategy algo_selection_strategy,
-                                 bool reproducible = true) const {
-            return (!reproducible ||
-                    contain_attribute(AlgoAttribute::REPRODUCIBLE)) &&
+        bool usable_attribute(
+                const NCBKernSizeParam& param,
+                AlgoSelectionStrategy algo_selection_strategy,
+                const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE) const {
+            return contain_attribute(attr) &&
                    usable(param, algo_selection_strategy);
         }
 
@@ -363,7 +361,7 @@ protected:
 
     virtual Algorithm* get_algorithm_heuristic_with_ncb(
             const NCBKernSizeParam& param, size_t workspace_limit_in_bytes,
-            bool reproducible = false);
+            const AlgoAttribute& attr);
 
     const char* get_algorithm_set_name() const override;
 

dnn/src/fallback/convolution/opr_impl.cpp

@@ -198,13 +198,13 @@ std::vector<ConvolutionImpl::Algorithm*> ConvolutionImpl::get_all_algorithms(
 ConvolutionImpl::Algorithm* ConvolutionImpl::get_algorithm_heuristic(
         const TensorLayout& src, const TensorLayout& filter,
         const TensorLayout& dst, size_t workspace_limit_in_bytes,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     auto fparam = make_ncb_kern_size_param(src, filter, dst, nullptr);
     auto result = get_algorithm_heuristic_with_ncb(
-            fparam, workspace_limit_in_bytes, reproducible);
+            fparam, workspace_limit_in_bytes, attr);
     if (result == nullptr) {
         result = naive::ConvolutionForwardImpl::get_algorithm_heuristic(
-                src, filter, dst, workspace_limit_in_bytes, reproducible);
+                src, filter, dst, workspace_limit_in_bytes, attr);
     }
     return result;
 }
@@ -312,18 +312,16 @@ void ConvolutionImpl::exec_with_ncb_kern(const NCBKernParam& param,
 
 ConvolutionImpl::Algorithm* ConvolutionImpl::get_algorithm_heuristic_with_ncb(
         const NCBKernSizeParam& param, size_t workspace_limit_in_bytes,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     auto algo_data_type = param.deduce_algo_data_type();
     auto suggest_category_order = suggest_algo_category_order(param);
     for (auto category : suggest_category_order) {
         auto&& origin_algos = select_algo_type({algo_data_type, category});
         ConvolutionImpl::Algorithm* heuristic_algo = nullptr;
         for (auto i : origin_algos) {
-            bool usable_reproducible =
-                    static_cast<AlgoBase*>(i)->usable_reproducible(
-                            param, AlgoSelectionStrategy::HEURISTIC,
-                            reproducible);
-            if (usable_reproducible &&
+            bool usable_attribute = static_cast<AlgoBase*>(i)->usable_attribute(
+                    param, AlgoSelectionStrategy::HEURISTIC, attr);
+            if (usable_attribute &&
                 static_cast<AlgoBase*>(i)->get_workspace(param) <=
                         workspace_limit_in_bytes) {
                 //! store the first usable algo if no prefer algo, choose it as
@@ -392,8 +390,8 @@ ConvolutionImpl::Algorithm* ConvolutionImpl::get_algorithm(
     }
     if (!m_prev_selected_algo ||
         memcmp(&m_prev_selected_algo_sizep, &param, sizeof(NCBKernSizeParam))) {
-        m_prev_selected_algo =
-                get_algorithm_heuristic_with_ncb(param, workspace_size);
+        m_prev_selected_algo = get_algorithm_heuristic_with_ncb(
+                param, workspace_size, AlgoAttribute::DEFAULT);
         m_prev_selected_algo_sizep = param;
     }
     return m_prev_selected_algo;
@@ -515,15 +513,15 @@ ConvolutionBackwardDataImpl::Algorithm*
 ConvolutionBackwardDataImpl::get_algorithm_heuristic(
         const TensorLayout& filter, const TensorLayout& diff,
         const TensorLayout& grad, size_t workspace_limit_in_bytes,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     if (param().format == param::Convolution::Format::NHWCD4 ||
         param().format == param::Convolution::Format::NCHW4) {
         return naive::ConvolutionBackwardDataImpl::get_algorithm_heuristic(
-                filter, diff, grad, workspace_limit_in_bytes, reproducible);
+                filter, diff, grad, workspace_limit_in_bytes, attr);
     }
     auto fparam = make_ncb_kern_size_param(filter, diff, grad);
     return get_algorithm_heuristic_with_ncb(fparam, workspace_limit_in_bytes,
-                                            reproducible);
+                                            attr);
 }
 
 ConvolutionBackwardDataImpl::NCBKernSizeParam
@@ -668,15 +666,15 @@ ConvolutionBackwardDataImpl::get_all_algorithms_with_ncb(
 ConvolutionBackwardDataImpl::Algorithm*
 ConvolutionBackwardDataImpl::get_algorithm_heuristic_with_ncb(
         const NCBKernSizeParam& param, size_t workspace_limit_in_bytes,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     if (param.filter_meta.group != 1) {
         auto p1g = param;
         p1g.filter_meta.group = 1;
         return ncb_1g_get_algorithm_heuristic(p1g, workspace_limit_in_bytes,
-                                              reproducible);
+                                              attr);
     }
     return ncb_1g_get_algorithm_heuristic(param, workspace_limit_in_bytes,
-                                          reproducible);
+                                          attr);
 }
 
 size_t ConvolutionBackwardDataImpl::ncb_1g_get_workspace(
@@ -731,14 +729,10 @@ ConvolutionBackwardDataImpl::ncb_1g_get_all_algorithms(
 ConvolutionBackwardDataImpl::Algorithm*
 ConvolutionBackwardDataImpl::ncb_1g_get_algorithm_heuristic(
         const NCBKernSizeParam& param, size_t workspace_limit_in_bytes,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     for (auto i : ncb_1g_get_all_algorithms(param)) {
         if (ncb_1g_get_workspace(i, param) <= workspace_limit_in_bytes) {
-            if (reproducible) {
-                if (i->contain_attribute(AlgoAttribute::REPRODUCIBLE)) {
-                    return i;
-                }
-            } else {
+            if (i->contain_attribute(attr)) {
                 return i;
             }
         }
@@ -788,7 +782,8 @@ ConvolutionBackwardDataImpl::get_algorithm(const NCBKernSizeParam& param) {
     if (!m_prev_selected_algo ||
         memcmp(&m_prev_selected_algo_sizep, &param, sizeof(NCBKernSizeParam))) {
         m_prev_selected_algo = ncb_1g_get_algorithm_heuristic(
-                param, std::numeric_limits<size_t>::max());
+                param, std::numeric_limits<size_t>::max(),
+                AlgoAttribute::DEFAULT);
         m_prev_selected_algo_sizep = param;
     }
     return m_prev_selected_algo;

dnn/src/fallback/convolution/opr_impl.h

@@ -90,7 +90,7 @@ public:
                                        const TensorLayout& filter,
                                        const TensorLayout& dst,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
+                                       const AlgoAttribute& attr) override;
 
     //! size param for kernels with non-contiguous batch
     struct NCBKernSizeParam {
@@ -238,11 +238,11 @@ public:
             return false;
         }
 
-        bool usable_reproducible(const NCBKernSizeParam& param,
-                                 AlgoSelectionStrategy algo_selection_strategy,
-                                 bool reproducible = true) const {
-            return (!reproducible ||
-                    contain_attribute(AlgoAttribute::REPRODUCIBLE)) &&
+        bool usable_attribute(
+                const NCBKernSizeParam& param,
+                AlgoSelectionStrategy algo_selection_strategy,
+                const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE) const {
+            return contain_attribute(attr) &&
                    usable(param, algo_selection_strategy);
         }
 
@@ -272,7 +272,7 @@ protected:
 
     virtual Algorithm* get_algorithm_heuristic_with_ncb(
             const NCBKernSizeParam& param, size_t workspace_limit_in_bytes,
-            bool reproducible = false);
+            const AlgoAttribute& attr);
 
     const char* get_algorithm_set_name() const override;
 
@@ -326,7 +326,7 @@ public:
                                        const TensorLayout& diff,
                                        const TensorLayout& grad,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
+                                       const AlgoAttribute& attr) override;
     const char* get_algorithm_set_name() const override;
 
     //! size param for kernels with non-contiguous batch
@@ -421,12 +421,10 @@ protected:
         virtual ncb_kern_t dispatch_kern(
                 ConvolutionBackwardDataImpl* opr,
                 const NCBKernSizeParam& param) const = 0;
-        bool usable_reproducible(ConvolutionBackwardDataImpl* opr,
-                                 const NCBKernSizeParam& param,
-                                 bool reproducible = true) const {
-            return (!reproducible ||
-                    contain_attribute(AlgoAttribute::REPRODUCIBLE)) &&
-                   usable(opr, param);
+        bool usable_attribute(
+                ConvolutionBackwardDataImpl* opr, const NCBKernSizeParam& param,
+                const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE) const {
+            return contain_attribute(attr) && usable(opr, param);
         }
         virtual bool is_preferred(const NCBKernSizeParam&) const {
             return false;
@@ -451,7 +449,7 @@ protected:
     //! default impl calls ncb_1g_get_algorithm_heuristic()
     virtual Algorithm* get_algorithm_heuristic_with_ncb(
             const NCBKernSizeParam& param, size_t workspace_limit_in_bytes,
-            bool reproducible = false);
+            const AlgoAttribute& attr);
 
     //! get kernel pointer for float32 non-contiguous batch 1-group kernel
     virtual ncb_kern_t ncb_1g_dispatch_kern(Algorithm* algo,
@@ -469,7 +467,7 @@ protected:
      */
     virtual Algorithm* ncb_1g_get_algorithm_heuristic(
             const NCBKernSizeParam& param, size_t workspace_limit_in_bytes,
-            bool reproducible = false);
+            const AlgoAttribute& attr);
 
     static bool is_matrix_mul_preferred(const NCBKernSizeParam& param);
     /**

dnn/src/fallback/matrix_mul/opr_impl.cpp

@@ -131,19 +131,20 @@ MatrixMulImpl::Algorithm* MatrixMulImpl::get_algorithm_from_desc(
 
 MatrixMul::Algorithm* MatrixMulImpl::get_algorithm_heuristic(
         const TensorLayout& A, const TensorLayout& B, const TensorLayout& C,
-        size_t workspace_limit_in_bytes, bool reproducible) {
+        size_t workspace_limit_in_bytes, const AlgoAttribute& attr) {
     auto kern_size_param = make_kern_size_param(A, B, C);
     if (auto algo = static_cast<AlgoBase*>(
                 get_algorithm_from_desc(execution_policy().algo))) {
         megdnn_assert(algo->get_workspace(kern_size_param) <
                       workspace_limit_in_bytes);
-        auto cur = megdnn::get_reproducible_algo<MatrixMulImpl>(algo,
-                                                                reproducible);
+        auto cur = megdnn::get_algo_with_attribute<MatrixMulImpl>(algo, attr);
         if (cur)
             return cur;
-        megdnn_throw(
-                "require reproducible algorithm, but given algorithm is not "
-                "reproducible");
+        megdnn_throw(ssprintf(
+                "require algorithm with attribute%s, but given algorithm with "
+                "attribute%s",
+                Algorithm::attribute_str(attr).c_str(),
+                Algorithm::attribute_str(algo->attribute()).c_str()));
     }
     AlgoTypePack algo_type;
     algo_type.data_type = kern_size_param.deduce_algo_data_type();
@@ -155,8 +156,8 @@ MatrixMul::Algorithm* MatrixMulImpl::get_algorithm_heuristic(
         if (static_cast<AlgoBase*>(algo)->usable(kern_size_param) &&
             static_cast<AlgoBase*>(algo)->get_workspace(kern_size_param) <=
                     workspace_limit_in_bytes) {
-            if (static_cast<AlgoBase*>(algo)->preferred_reproducible(
-                        kern_size_param, reproducible)) {
+            if (static_cast<AlgoBase*>(algo)->preferred_attribute(
+                        kern_size_param, attr)) {
                 //! use gemv algo if it's prefered
                 if (algo->algoset() == AlgoBase::AlgoSet::ALGO_TYPE_GEMV) {
                     return algo;
@@ -214,8 +215,9 @@ MatrixMulImpl::KernParam MatrixMulImpl::make_kern_param(
 size_t MatrixMulImpl::get_workspace_in_bytes(const TensorLayout& A,
                                              const TensorLayout& B,
                                              const TensorLayout& C) {
-    if (auto algo = get_algorithm_heuristic(
-                A, B, C, std::numeric_limits<size_t>::max(), false)) {
+    if (auto algo = get_algorithm_heuristic(A, B, C,
+                                            std::numeric_limits<size_t>::max(),
+                                            AlgoAttribute::DEFAULT)) {
         auto kern_size_param = make_kern_size_param(A, B, C);
         return static_cast<AlgoBase*>(algo)->get_workspace(kern_size_param);
     }
@@ -228,7 +230,7 @@ void MatrixMulImpl::exec(_megdnn_tensor_in A, _megdnn_tensor_in B,
 
     if (auto algo = get_algorithm_heuristic(A.layout, B.layout, C.layout,
                                             std::numeric_limits<size_t>::max(),
-                                            false)) {
+                                            AlgoAttribute::DEFAULT)) {
         auto kern_param = make_kern_param(A, B, C, workspace);
         auto kern = static_cast<AlgoBase*>(algo)->get_kern(kern_param);
         auto run = [kern, kern_param]() { kern(kern_param); };

dnn/src/fallback/matrix_mul/opr_impl.h

@@ -223,11 +223,10 @@ public:
         virtual InnerBlockSize get_inner_block_size() const {
             megdnn_assert(0);
         };
-        bool preferred_reproducible(const KernSizeParam& param,
-                                    bool reproducible = true) {
-            return (!reproducible ||
-                    contain_attribute(AlgoAttribute::REPRODUCIBLE)) &&
-                   preferred(param);
+        bool preferred_attribute(
+                const KernSizeParam& param,
+                const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE) {
+            return contain_attribute(attr) && preferred(param);
         };
         virtual MatmulDescription matmul_description() const = 0;
 
@@ -272,7 +271,7 @@ protected:
                                        const TensorLayout& B,
                                        const TensorLayout& C,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
+                                       const AlgoAttribute& attr) override;
 
 };
 

dnn/src/naive/batch_conv_bias/opr_impl.cpp

@@ -125,16 +125,14 @@ BatchConvBiasForwardImpl::get_algorithm_heuristic(
         const TensorLayout& /* bias */, const TensorLayout& /* z */,
         const TensorLayout& /* dst */, size_t /* workspace_limit_in_bytes */
         ,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     auto algo = static_cast<HandleImpl*>(handle())
             ->default_batch_conv_bias_fwd_algo();
-    if (reproducible) {
-        megdnn_assert(algo->contain_attribute(AlgoAttribute::REPRODUCIBLE),
-                      "require reproducible algorithm, but heuristic "
-                      "algorithm(%s) is not "
-                      "reproducible",
-                      algo->name());
-    }
+    megdnn_assert(algo->contain_attribute(attr),
+                  "require algorithm with attribute%s, but heuristic "
+                  "algorithm(%s) with attribute%s ",
+                  Algorithm::attribute_str(attr).c_str(), algo->name(),
+                  Algorithm::attribute_str(algo->attribute()).c_str());
     return algo;
 }
 

dnn/src/naive/batch_conv_bias/opr_impl.h

@@ -37,7 +37,7 @@ public:
                                        const TensorLayout& z,
                                        const TensorLayout& dst,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
+                                       const AlgoAttribute& attr) override;
 
     Algorithm* get_algorithm_from_desc(const AlgorithmDesc&) override;
 

dnn/src/naive/batched_matrix_mul/opr_impl.cpp

@@ -76,7 +76,7 @@ BatchedMatrixMulForward::Algorithm*
 BatchedMatrixMulForwardImpl::get_algorithm_heuristic(
         const TensorLayout& /*A*/, const TensorLayout& /*B*/,
         const TensorLayout& /*C*/, size_t /*workspace_limit_in_bytes*/,
-        bool /* reproducible */) {
+        const AlgoAttribute& /*attr*/) {
     return static_cast<HandleImpl*>(handle())
             ->default_batched_matmul_fwd_algo();
 }

dnn/src/naive/batched_matrix_mul/opr_impl.h

@@ -32,7 +32,7 @@ public:
                                        const TensorLayout& /*B*/,
                                        const TensorLayout& /*C*/,
                                        size_t /*workspace_limit_in_bytes*/,
-                                       bool /* reproducible */) override;
+                                       const AlgoAttribute& /*attr*/) override;
 
     Algorithm* get_algorithm_from_desc(const AlgorithmDesc&) override;
 

dnn/src/naive/conv_bias/opr_impl.cpp

@@ -246,16 +246,14 @@ ConvBiasForward::Algorithm* ConvBiasForwardImpl::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) {
+        const AlgoAttribute& attr) {
     auto algo =
             static_cast<HandleImpl*>(handle())->default_conv_bias_fwd_algo();
-    if (reproducible) {
-        megdnn_assert(algo->contain_attribute(AlgoAttribute::REPRODUCIBLE),
-                      "require reproducible algorithm, but heuristic "
-                      "algorithm(%s) is not "
-                      "reproducible",
-                      algo->name());
-    }
+    megdnn_assert(algo->contain_attribute(attr),
+                  "require algorithm with attribute%s, but heuristic "
+                  "algorithm(%s) with attribute%s ",
+                  Algorithm::attribute_str(attr).c_str(), algo->name(),
+                  Algorithm::attribute_str(algo->attribute()).c_str());
     return algo;
 }
 

dnn/src/naive/conv_bias/opr_impl.h

@@ -37,7 +37,7 @@ public:
                                        const TensorLayout& z,
                                        const TensorLayout& dst,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
+                                       const AlgoAttribute& attr) override;
 
     size_t get_workspace_in_bytes(
             const TensorLayout& src, const TensorLayout& filter,

dnn/src/naive/convolution/convolution.cpp

@@ -272,16 +272,14 @@ ConvolutionForwardImpl:: get_all_algorithms(const TensorLayout &,
 ConvolutionForward::Algorithm* ConvolutionForwardImpl::get_algorithm_heuristic(
         const TensorLayout& /* src */, const TensorLayout& /* diff */,
         const TensorLayout& /* grad */, size_t /* workspace_limit_in_bytes */,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     auto algo =
             static_cast<HandleImpl*>(handle())->default_conv_fwd_algo();
-    if (reproducible) {
-        megdnn_assert(algo->contain_attribute(AlgoAttribute::REPRODUCIBLE),
-                      "require reproducible algorithm, but heuristic "
-                      "algorithm(%s) is not "
-                      "reproducible",
-                      algo->name());
-    }
+    megdnn_assert(algo->contain_attribute(attr),
+                  "require algorithm with attribute%s, but heuristic "
+                  "algorithm(%s) with attribute%s ",
+                  Algorithm::attribute_str(attr).c_str(), algo->name(),
+                  Algorithm::attribute_str(algo->attribute()).c_str());
     return algo;
 }
 
@@ -304,16 +302,14 @@ ConvolutionBackwardData::Algorithm*
 ConvolutionBackwardDataImpl::get_algorithm_heuristic(
         const TensorLayout& /* filter */, const TensorLayout& /* diff */,
         const TensorLayout& /* grad */, size_t /* workspace_limit_in_bytes */,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     auto algo =
             static_cast<HandleImpl*>(handle())->default_conv_bwd_data_algo();
-    if (reproducible) {
-        megdnn_assert(algo->contain_attribute(AlgoAttribute::REPRODUCIBLE),
-                      "require reproducible algorithm, but heuristic "
-                      "algorithm(%s) is not "
-                      "reproducible",
-                      algo->name());
-    }
+    megdnn_assert(algo->contain_attribute(attr),
+                  "require algorithm with attribute%s, but heuristic "
+                  "algorithm(%s) with attribute%s ",
+                  Algorithm::attribute_str(attr).c_str(), algo->name(),
+                  Algorithm::attribute_str(algo->attribute()).c_str());
     return algo;
 }
 
@@ -337,16 +333,14 @@ ConvolutionBackwardFilter::Algorithm*
 ConvolutionBackwardFilterImpl::get_algorithm_heuristic(
         const TensorLayout& /* src */, const TensorLayout& /* diff */,
         const TensorLayout& /* grad */, size_t /* workspace_limit_in_bytes */,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     auto algo =
             static_cast<HandleImpl*>(handle())->default_conv_bwd_filter_algo();
-    if (reproducible) {
-        megdnn_assert(algo->contain_attribute(AlgoAttribute::REPRODUCIBLE),
-                      "require reproducible algorithm, but heuristic "
-                      "algorithm(%s) is not "
-                      "reproducible",
-                      algo->name());
-    }
+    megdnn_assert(algo->contain_attribute(attr),
+                  "require algorithm with attribute%s, but heuristic "
+                  "algorithm(%s) with attribute%s ",
+                  Algorithm::attribute_str(attr).c_str(), algo->name(),
+                  Algorithm::attribute_str(algo->attribute()).c_str());
     return algo;
 }
 

dnn/src/naive/convolution/opr_impl.h

@@ -29,7 +29,7 @@ class ConvolutionForwardImpl: public ConvolutionForward {
                                            const TensorLayout& filter,
                                            const TensorLayout& dst,
                                            size_t workspace_limit_in_bytes,
-                                           bool reproducible) override;
+                                           const AlgoAttribute& attr) override;
         size_t get_workspace_in_bytes(const TensorLayout&, const TensorLayout&,
                                       const TensorLayout&,
                                       const PreprocessedFilter*) override {
@@ -71,7 +71,7 @@ class ConvolutionBackwardDataImpl: public ConvolutionBackwardData {
                                            const TensorLayout& diff,
                                            const TensorLayout& grad,
                                            size_t workspace_limit_in_bytes,
-                                           bool reproducible) override;
+                                           const AlgoAttribute& attr) override;
         size_t get_workspace_in_bytes(const TensorLayout&, const TensorLayout&,
                                       const TensorLayout&) override;
 
@@ -94,7 +94,7 @@ class ConvolutionBackwardFilterImpl: public ConvolutionBackwardFilter {
                                            const TensorLayout& diff,
                                            const TensorLayout& grad,
                                            size_t workspace_limit_in_bytes,
-                                           bool reproducible) override;
+                                           const AlgoAttribute& attr) override;
         size_t get_workspace_in_bytes(const TensorLayout&, const TensorLayout&,
                                       const TensorLayout&) override;
 

dnn/src/naive/convolution3d/convolution3d.cpp

@@ -120,15 +120,13 @@ Convolution3DForward::Algorithm*
 Convolution3DForwardImpl::get_algorithm_heuristic(
         const TensorLayout& /* src */, const TensorLayout& /* filter */,
         const TensorLayout& /* dst */, size_t /* workspace_limit_in_bytes */,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     auto algo = static_cast<HandleImpl*>(handle())->default_conv3d_fwd_algo();
-    if (reproducible) {
-        megdnn_assert(algo->contain_attribute(AlgoAttribute::REPRODUCIBLE),
-                      "require reproducible algorithm, but heuristic "
-                      "algorithm(%s) is not "
-                      "reproducible",
-                      algo->name());
-    }
+    megdnn_assert(algo->contain_attribute(attr),
+                  "require algorithm with attribute%s, but heuristic "
+                  "algorithm(%s) with attribute%s ",
+                  Algorithm::attribute_str(attr).c_str(), algo->name(),
+                  Algorithm::attribute_str(algo->attribute()).c_str());
     return algo;
 }
 
@@ -152,16 +150,14 @@ Convolution3DBackwardData::Algorithm*
 Convolution3DBackwardDataImpl::get_algorithm_heuristic(
         const TensorLayout& /* filter */, const TensorLayout& /* diff */,
         const TensorLayout& /* grad */, size_t /* workspace_limit_in_bytes */,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     auto algo =
             static_cast<HandleImpl*>(handle())->default_conv3d_bwd_data_algo();
-    if (reproducible) {
-        megdnn_assert(algo->contain_attribute(AlgoAttribute::REPRODUCIBLE),
-                      "require reproducible algorithm, but heuristic "
-                      "algorithm(%s) is not "
-                      "reproducible",
-                      algo->name());
-    }
+    megdnn_assert(algo->contain_attribute(attr),
+                  "require algorithm with attribute%s, but heuristic "
+                  "algorithm(%s) with attribute%s ",
+                  Algorithm::attribute_str(attr).c_str(), algo->name(),
+                  Algorithm::attribute_str(algo->attribute()).c_str());
     return algo;
 }
 
@@ -187,16 +183,14 @@ Convolution3DBackwardFilterImpl::get_algorithm_heuristic(
         const TensorLayout& /* src */, const TensorLayout& /* diff */,
         const TensorLayout& /* grad */, size_t /* workspace_limit_in_bytes */
         ,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     auto algo = static_cast<HandleImpl*>(handle())
                         ->default_conv3d_bwd_filter_algo();
-    if (reproducible) {
-        megdnn_assert(algo->contain_attribute(AlgoAttribute::REPRODUCIBLE),
-                      "require reproducible algorithm, but heuristic "
-                      "algorithm(%s) is not "
-                      "reproducible",
-                      algo->name());
-    }
+    megdnn_assert(algo->contain_attribute(attr),
+                  "require algorithm with attribute%s, but heuristic "
+                  "algorithm(%s) with attribute%s ",
+                  Algorithm::attribute_str(attr).c_str(), algo->name(),
+                  Algorithm::attribute_str(algo->attribute()).c_str());
     return algo;
 }
 

dnn/src/naive/convolution3d/opr_impl.h

@@ -26,7 +26,7 @@ public:
                                        const TensorLayout& filter,
                                        const TensorLayout& dst,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
+                                       const AlgoAttribute& attr) override;
     size_t get_workspace_in_bytes(const TensorLayout&, const TensorLayout&,
                                   const TensorLayout&) override {
         return 0;
@@ -48,7 +48,7 @@ public:
                                        const TensorLayout& diff,
                                        const TensorLayout& grad,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
+                                       const AlgoAttribute& attr) override;
     size_t get_workspace_in_bytes(const TensorLayout&, const TensorLayout&,
                                   const TensorLayout&) override {
         return 0;
@@ -70,7 +70,7 @@ public:
                                        const TensorLayout& diff,
                                        const TensorLayout& grad,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
+                                       const AlgoAttribute& attr) override;
     size_t get_workspace_in_bytes(const TensorLayout&, const TensorLayout&,
                                   const TensorLayout&) override {
         return 0;

dnn/src/naive/deformable_conv/opr_impl.h

@@ -32,7 +32,7 @@ public:
                                        const TensorLayout& /* mask */,
                                        const TensorLayout& /* dst */,
                                        size_t /* workspace_limit_in_bytes */,
-                                       bool /* reproducible */) override {
+                                       const AlgoAttribute& /*attr*/) override {
         return nullptr;
     };
 
@@ -74,7 +74,7 @@ public:
                                        const TensorLayout& /* out_grad */,
                                        const TensorLayout& /* filter_grad */,
                                        size_t /* workspace_limit_in_bytes */,
-                                       bool /* reproducible */) override {
+                                       const AlgoAttribute& /*attr*/) override {
         return nullptr;
     };
 
@@ -121,7 +121,7 @@ public:
                                        const TensorLayout& /* offset_grad */,
                                        const TensorLayout& /* mask_grad */,
                                        size_t /* workspace_limit_in_bytes */,
-                                       bool /* reproducible */) override {
+                                       const AlgoAttribute& /*attr*/) override {
         return nullptr;
     };
 

dnn/src/naive/local_share/opr_impl.cpp

@@ -162,16 +162,14 @@ LocalShareForwardImpl::get_all_algorithms(const TensorLayout&,
 LocalShareForward::Algorithm* LocalShareForwardImpl::get_algorithm_heuristic(
         const TensorLayout& /* src */, const TensorLayout& /* diff */,
         const TensorLayout& /* grad */, size_t /* workspace_limit_in_bytes */,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     auto algo =
             static_cast<HandleImpl*>(handle())->default_local_share_fwd_algo();
-    if (reproducible) {
-        megdnn_assert(algo->contain_attribute(AlgoAttribute::REPRODUCIBLE),
-                      "require reproducible algorithm, but heuristic "
-                      "algorithm(%s) is not "
-                      "reproducible",
-                      algo->name());
-    }
+    megdnn_assert(algo->contain_attribute(attr),
+                  "require algorithm with attribute%s, but heuristic "
+                  "algorithm(%s) with attribute%s ",
+                  Algorithm::attribute_str(attr).c_str(), algo->name(),
+                  Algorithm::attribute_str(algo->attribute()).c_str());
     return algo;
 }
 
@@ -196,16 +194,14 @@ LocalShareBackwardData::Algorithm*
 LocalShareBackwardDataImpl::get_algorithm_heuristic(
         const TensorLayout& /* filter */, const TensorLayout& /* diff */,
         const TensorLayout& /* grad */, size_t /* workspace_limit_in_bytes */,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     auto algo = static_cast<HandleImpl*>(handle())
                         ->default_local_share_bwd_data_algo();
-    if (reproducible) {
-        megdnn_assert(algo->contain_attribute(AlgoAttribute::REPRODUCIBLE),
-                      "require reproducible algorithm, but heuristic "
-                      "algorithm(%s) is not "
-                      "reproducible",
-                      algo->name());
-    }
+    megdnn_assert(algo->contain_attribute(attr),
+                  "require algorithm with attribute%s, but heuristic "
+                  "algorithm(%s) with attribute%s ",
+                  Algorithm::attribute_str(attr).c_str(), algo->name(),
+                  Algorithm::attribute_str(algo->attribute()).c_str());
     return algo;
 }
 
@@ -230,16 +226,14 @@ LocalShareBackwardFilter::Algorithm*
 LocalShareBackwardFilterImpl::get_algorithm_heuristic(
         const TensorLayout& /* src */, const TensorLayout& /* diff */,
         const TensorLayout& /* grad */, size_t /* workspace_limit_in_bytes */,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     auto algo = static_cast<HandleImpl*>(handle())
                         ->default_local_share_bwd_filter_algo();
-    if (reproducible) {
-        megdnn_assert(algo->contain_attribute(AlgoAttribute::REPRODUCIBLE),
-                      "require reproducible algorithm, but heuristic "
-                      "algorithm(%s) is not "
-                      "reproducible",
-                      algo->name());
-    }
+    megdnn_assert(algo->contain_attribute(attr),
+                  "require algorithm with attribute%s, but heuristic "
+                  "algorithm(%s) with attribute%s ",
+                  Algorithm::attribute_str(attr).c_str(), algo->name(),
+                  Algorithm::attribute_str(algo->attribute()).c_str());
     return algo;
 }
 

dnn/src/naive/local_share/opr_impl.h

@@ -34,7 +34,7 @@ public:
                                        const TensorLayout& /*filter*/,
                                        const TensorLayout& /*dst*/,
                                        size_t /*workspace_limit_in_bytes*/,
-                                       bool /*reproducible*/) override;
+                                       const AlgoAttribute& /*attr*/) override;
 
     Algorithm* get_algorithm_from_desc(const AlgorithmDesc&) override;
     const char* get_algorithm_set_name() const override { return "DEFAULT"; }
@@ -59,7 +59,7 @@ public:
                                        const TensorLayout& /*diff*/,
                                        const TensorLayout& /*grad*/,
                                        size_t /*workspace_limit_in_bytes*/,
-                                       bool /*reproducible*/) override;
+                                       const AlgoAttribute& /*attr*/) override;
 
     Algorithm* get_algorithm_from_desc(const AlgorithmDesc&) override;
     const char* get_algorithm_set_name() const override { return "DEFAULT"; }
@@ -84,7 +84,7 @@ public:
                                        const TensorLayout& /*diff*/,
                                        const TensorLayout& /*grad*/,
                                        size_t /*workspace_limit_in_bytes*/,
-                                       bool /*reproducible*/) override;
+                                       const AlgoAttribute& /*attr*/) override;
 
     Algorithm* get_algorithm_from_desc(const AlgorithmDesc&) override;
     const char* get_algorithm_set_name() const override { return "DEFAULT"; }

dnn/src/naive/matrix_mul/opr_impl.cpp

@@ -91,7 +91,7 @@ MatrixMulForwardImpl::get_all_algorithms(const TensorLayout& /*A*/,
 MatrixMulForward::Algorithm* MatrixMulForwardImpl::get_algorithm_heuristic(
         const TensorLayout& /*A*/, const TensorLayout& /*B*/,
         const TensorLayout& /*C*/, size_t /*workspace_limit_in_bytes*/,
-        bool /* reproducible */) {
+        const AlgoAttribute& /*attr*/) {
     return static_cast<HandleImpl*>(handle())->default_matmul_fwd_algo();
 }
 

dnn/src/naive/matrix_mul/opr_impl.h

@@ -33,7 +33,7 @@ public:
                                        const TensorLayout& /*B*/,
                                        const TensorLayout& /*C*/,
                                        size_t /*workspace_limit_in_bytes*/,
-                                       bool /* reproducible */) override;
+                                       const AlgoAttribute& /*attr*/) override;
 
     Algorithm* get_algorithm_from_desc(const AlgorithmDesc&) override;
 

dnn/src/rocm/batched_matrix_mul/algos.h

@@ -70,12 +70,11 @@ public:
     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,
+    bool is_available_attribute(
+            const SizeArgs& args,
+            const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE,
             size_t limit = std::numeric_limits<size_t>::max()) const {
-        return (!reproducible ||
-                contain_attribute(AlgoAttribute::REPRODUCIBLE)) &&
-               is_available_wk(args, limit);
+        return contain_attribute(attr) && is_available_wk(args, limit);
     }
     AlgoBase& check_workspace(const SizeArgs& args,
                               const Workspace& workspace) {

dnn/src/rocm/batched_matrix_mul/opr_impl.cpp

@@ -32,16 +32,16 @@ BatchedMatrixMulForwardImpl::get_all_algorithms(const TensorLayout& A,
 BatchedMatrixMulForwardImpl::Algorithm*
 BatchedMatrixMulForwardImpl::get_algorithm_heuristic(
         const TensorLayout& A, const TensorLayout& B, const TensorLayout& C,
-        size_t workspace_limit_in_bytes, bool reproducible) {
+        size_t workspace_limit_in_bytes, const AlgoAttribute& attr) {
     AlgoBase::SizeArgs args{this, A, B, C};
-    if (sm_algo_pack.blas.is_available_reproducible(args, reproducible,
-                                                    workspace_limit_in_bytes)) {
+    if (sm_algo_pack.blas.is_available_attribute(args, attr,
+                                                 workspace_limit_in_bytes)) {
         return &sm_algo_pack.blas;
     }
-    if (reproducible) {
-        return megdnn::get_reproducible_algo<BatchedMatrixMulForwardImpl>(
+    if (attr != AlgoAttribute::DEFAULT) {
+        return megdnn::get_algo_with_attribute<BatchedMatrixMulForwardImpl>(
                 sm_algo_pack.all_algos, args, workspace_limit_in_bytes,
-                "batched matrix mul forward");
+                "batched matrix mul forward", attr);
     } else {
         return megdnn::get_usable_algo<BatchedMatrixMulForwardImpl>(
                 sm_algo_pack.all_algos, args, workspace_limit_in_bytes,

dnn/src/rocm/batched_matrix_mul/opr_impl.h

@@ -40,7 +40,7 @@ private:
                                        const TensorLayout& /*B*/,
                                        const TensorLayout& /*C*/,
                                        size_t /*workspace_limit_in_bytes*/,
-                                       bool /*reproducible*/) override;
+                                       const AlgoAttribute& /*attr*/) override;
 
     const char* get_algorithm_set_name() const override {
         return "ROCM BATCHED MATMUL";

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

@@ -74,12 +74,11 @@ public:
     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,
+    bool is_available_attribute(
+            const SizeArgs& args,
+            const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE,
             size_t limit = std::numeric_limits<size_t>::max()) {
-        return (!reproducible ||
-                contain_attribute(AlgoAttribute::REPRODUCIBLE)) &&
-               is_available_wk(args, limit);
+        return contain_attribute(attr) && is_available_wk(args, limit);
     }
 
     AlgoBase& check_workspace(const SizeArgs& args,
@@ -96,24 +95,20 @@ public:
 };
 
 class ConvolutionBackwardDataImpl::AlgoMIOpen final : public AlgoBase {
-    bool m_is_reproducible;
+    AlgoAttribute m_algo_attribute;
     const char* m_name;
 
     miopenConvBwdDataAlgorithm_t find_best_algo(const ExecArgs& args);
 
 public:
     AlgoMIOpen() = delete;
-    AlgoMIOpen(bool is_reproducible) : m_is_reproducible(is_reproducible) {}
+    AlgoMIOpen(AlgoAttribute attr) : m_algo_attribute(attr) {}
 
     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;
     AlgoAttribute attribute() const override {
-        auto ret = static_cast<AlgoAttribute>(0);
-        if (m_is_reproducible) {
-            ret |= AlgoAttribute::REPRODUCIBLE;
-        }
-        return ret;
+        return m_algo_attribute;
     }
 
     const char* name() const override {
@@ -124,7 +119,7 @@ public:
     MEGDNN_DECL_ALGO_TYPE(ROCM_MIOPEN)
     std::string param() const override {
         std::string ret;
-        serialize_write_pod(m_is_reproducible, ret);
+        serialize_write_pod(m_algo_attribute, ret);
         return ret;
     }
 
@@ -170,7 +165,7 @@ class ConvolutionBackwardDataImpl::AlgoPack : NonCopyableObj {
 public:
     AlgoPack();
 
-    AlgoMIOpen miopen{true};
+    AlgoMIOpen miopen{AlgoAttribute::REPRODUCIBLE};
     AlgoMatmul matmul;
     AlgoChanwise chanwise;
 

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

@@ -71,12 +71,11 @@ public:
     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,
+    bool is_available_attribute(
+            const SizeArgs& args,
+            const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE,
             size_t limit = std::numeric_limits<size_t>::max()) {
-        return (!reproducible ||
-                contain_attribute(AlgoAttribute::REPRODUCIBLE)) &&
-               is_available_wk(args, limit);
+        return contain_attribute(attr) && is_available_wk(args, limit);
     }
 
     AlgoBase& check_workspace(const SizeArgs& args,
@@ -93,25 +92,21 @@ public:
 };
 
 class ConvolutionBackwardFilterImpl::AlgoMIOpen final : public AlgoBase {
-    bool m_is_reproducible;
+    AlgoAttribute m_algo_attribute;
     const char* m_name;
 
     miopenConvBwdWeightsAlgorithm_t find_best_algo(const ExecArgs& args);
 
 public:
     AlgoMIOpen() = delete;
-    AlgoMIOpen(bool is_reproducible) : m_is_reproducible(is_reproducible) {}
+    AlgoMIOpen(AlgoAttribute attr) : m_algo_attribute(attr) {}
 
     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;
 
     AlgoAttribute attribute() const override {
-        auto ret = static_cast<AlgoAttribute>(0);
-        if (m_is_reproducible) {
-            ret |= AlgoAttribute::REPRODUCIBLE;
-        }
-        return ret;
+        return m_algo_attribute;
     }
     const char* name() const override {
         return "MIOpenConvolutionBackwardFilter";
@@ -121,7 +116,7 @@ public:
     MEGDNN_DECL_ALGO_TYPE(ROCM_MIOPEN)
     std::string param() const override {
         std::string ret;
-        serialize_write_pod(m_is_reproducible, ret);
+        serialize_write_pod(m_algo_attribute, ret);
         return ret;
     }
 
@@ -166,7 +161,7 @@ class ConvolutionBackwardFilterImpl::AlgoPack : NonCopyableObj {
 public:
     AlgoPack();
 
-    AlgoMIOpen miopen{true};
+    AlgoMIOpen miopen{AlgoAttribute::REPRODUCIBLE};
     AlgoMatmul matmul;
     AlgoChanwise chanwise;
 

dnn/src/rocm/convolution/forward/algo.h

@@ -73,12 +73,11 @@ public:
     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,
+    bool is_available_attribute(
+            const SizeArgs& args,
+            const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE,
             size_t limit = std::numeric_limits<size_t>::max()) {
-        return (!reproducible ||
-                contain_attribute(AlgoAttribute::REPRODUCIBLE)) &&
-               is_available_wk(args, limit);
+        return contain_attribute(attr) && is_available_wk(args, limit);
     }
 
     AlgoBase& check_workspace(const SizeArgs& args,
@@ -94,25 +93,21 @@ public:
 };
 
 class ConvolutionForwardImpl::AlgoMIOpen final : public AlgoBase {
-    bool m_is_reproducible;
+    AlgoAttribute m_algo_attribute;
     const char* m_name;
 
     miopenConvFwdAlgorithm_t find_best_algo(const ExecArgs& args);
 
 public:
     AlgoMIOpen() = delete;
-    AlgoMIOpen(bool is_reproducible) : m_is_reproducible(is_reproducible) {}
+    AlgoMIOpen(AlgoAttribute attr) : m_algo_attribute(attr) {}
 
     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;
 
     AlgoAttribute attribute() const override {
-        auto ret = static_cast<AlgoAttribute>(0);
-        if (m_is_reproducible) {
-            ret |= AlgoAttribute::REPRODUCIBLE;
-        }
-        return ret;
+        return m_algo_attribute;
     }
 
     const char* name() const override { return "MIOpenConvolutionForward"; }
@@ -121,7 +116,7 @@ public:
     MEGDNN_DECL_ALGO_TYPE(ROCM_MIOPEN)
     std::string param() const override {
         std::string ret;
-        serialize_write_pod(m_is_reproducible, ret);
+        serialize_write_pod(m_algo_attribute, ret);
         return ret;
     }
 
@@ -215,7 +210,7 @@ class ConvolutionForwardImpl::AlgoPack : NonCopyableObj {
 public:
     AlgoPack();
 
-    AlgoMIOpen miopen{true};
+    AlgoMIOpen miopen{AlgoAttribute::REPRODUCIBLE};
     AlgoMatmul matmul;
     AlgoInplaceMatmul inplace_matmul;
     Algo1x1 a1x1;

dnn/src/rocm/convolution/opr_impl.cpp

@@ -33,70 +33,69 @@ ConvolutionForwardImpl::get_algorithm_heuristic(const TensorLayout& src,
                                                 const TensorLayout& filter,
                                                 const TensorLayout& dst,
                                                 size_t workspace_limit_in_bytes,
-                                                bool reproducible) {
+                                                const AlgoAttribute& attr) {
     auto fm = check_layout_fwd(src, filter, dst);
     return get_algorithm_heuristic(src, fm, dst, workspace_limit_in_bytes,
-                                   reproducible);
+                                   attr);
 }
 
 ConvolutionForwardImpl::Algorithm*
 ConvolutionForwardImpl::get_algorithm_heuristic(
         const TensorLayout& src, const CanonizedFilterMeta& filter,
         const TensorLayout& dst, size_t workspace_limit_in_bytes,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     AlgoBase::SizeArgs args(this, src, filter, dst);
 
     //! MIOpen auto-tuning need to run with actual tensors, so we cannot get
     //! best algorithm here.
     if (is_miopen_supported(args)) {
-        auto algo = megdnn::get_reproducible_algo<ConvolutionForwardImpl>(
-                sm_algo_pack.miopen_algos[0], reproducible);
+        auto algo = megdnn::get_algo_with_attribute<ConvolutionForwardImpl>(
+                sm_algo_pack.miopen_algos[0], attr);
         if (algo)
             return algo;
     }
 
     if (args.filter_meta.group > 1) {
-        if (sm_algo_pack.chanwise.is_available_reproducible(
-                    args, reproducible, workspace_limit_in_bytes)) {
+        if (sm_algo_pack.chanwise.is_available_attribute(
+                    args, attr, workspace_limit_in_bytes)) {
             return &sm_algo_pack.chanwise;
         }
     }
 
-    auto prefer_1x1 = [&args, reproducible, workspace_limit_in_bytes]() {
+    auto prefer_1x1 = [&args, attr, workspace_limit_in_bytes]() {
         const size_t MAX_BATCH_SIZE_FOR_1x1_MAT_ALGO = 4;
         size_t batch_size = args.src_layout->shape[0];
 
         if (batch_size > MAX_BATCH_SIZE_FOR_1x1_MAT_ALGO) {
             return false;
         }
-        return sm_algo_pack.a1x1.is_available_reproducible(
-                args, reproducible, workspace_limit_in_bytes);
+        return sm_algo_pack.a1x1.is_available_attribute(
+                args, attr, workspace_limit_in_bytes);
     };
 
     if (prefer_1x1()) {
         return &sm_algo_pack.a1x1;
     }
 
-    auto prefer_1x1_large_batch = [&args, reproducible,
-                                   workspace_limit_in_bytes]() {
+    auto prefer_1x1_large_batch = [&args, attr, workspace_limit_in_bytes]() {
         const size_t MIN_BATCH_SIZE_FOR_1x1_LARGE_BATCH_ALGO = 32;
         size_t batch_size = args.src_layout->shape[0];
 
         if (batch_size < MIN_BATCH_SIZE_FOR_1x1_LARGE_BATCH_ALGO) {
             return false;
         }
-        return sm_algo_pack.batched_matrix_mul.is_available_reproducible(
-                args, reproducible, workspace_limit_in_bytes);
+        return sm_algo_pack.batched_matrix_mul.is_available_attribute(
+                args, attr, workspace_limit_in_bytes);
     };
 
     if (prefer_1x1_large_batch()) {
         return &sm_algo_pack.batched_matrix_mul;
     }
 
-    if (reproducible) {
-        return megdnn::get_reproducible_algo<ConvolutionForwardImpl>(
+    if (attr != AlgoAttribute::DEFAULT) {
+        return megdnn::get_algo_with_attribute<ConvolutionForwardImpl>(
                 sm_algo_pack.non_miopen_algos, args, workspace_limit_in_bytes,
-                "rocm conv fwd");
+                "rocm conv fwd", attr);
     } else {
         return megdnn::get_usable_algo<ConvolutionForwardImpl>(
                 sm_algo_pack.non_miopen_algos, args, workspace_limit_in_bytes,
@@ -157,36 +156,36 @@ ConvolutionBackwardDataImpl::Algorithm*
 ConvolutionBackwardDataImpl::get_algorithm_heuristic(
         const TensorLayout& filter, const TensorLayout& diff,
         const TensorLayout& grad, size_t workspace_limit_in_bytes,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     auto fm = check_layout_fwd(grad, filter, diff);
     return get_algorithm_heuristic(fm, diff, grad, workspace_limit_in_bytes,
-                                   reproducible);
+                                   attr);
 }
 
 ConvolutionBackwardDataImpl::Algorithm*
 ConvolutionBackwardDataImpl::get_algorithm_heuristic(
         const CanonizedFilterMeta& filter, const TensorLayout& diff,
         const TensorLayout& grad, size_t workspace_limit_in_bytes,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     AlgoBase::SizeArgs args(this, filter, diff, grad);
 
     if (is_miopen_supported(args.as_fwd_args())) {
-        auto algo = megdnn::get_reproducible_algo<ConvolutionBackwardDataImpl>(
-                sm_algo_pack.miopen_algos[0], reproducible);
+        auto algo = megdnn::get_algo_with_attribute<ConvolutionBackwardDataImpl>(
+                sm_algo_pack.miopen_algos[0], attr);
         if (algo)
             return algo;
     }
 
     if (args.filter_meta.group > 1 &&
-        sm_algo_pack.chanwise.is_available_reproducible(
-                args, reproducible, workspace_limit_in_bytes)) {
+        sm_algo_pack.chanwise.is_available_attribute(
+                args, attr, workspace_limit_in_bytes)) {
         return &sm_algo_pack.chanwise;
     }
 
-    if (reproducible) {
-        return megdnn::get_reproducible_algo<ConvolutionBackwardDataImpl>(
+    if (attr != AlgoAttribute::DEFAULT) {
+        return megdnn::get_algo_with_attribute<ConvolutionBackwardDataImpl>(
                 sm_algo_pack.non_miopen_algos, args, workspace_limit_in_bytes,
-                "rocm conv bwd_data");
+                "rocm conv bwd_data", attr);
     } else {
         return megdnn::get_usable_algo<ConvolutionBackwardDataImpl>(
                 sm_algo_pack.non_miopen_algos, args, workspace_limit_in_bytes,
@@ -230,38 +229,38 @@ ConvolutionBackwardFilterImpl::Algorithm*
 ConvolutionBackwardFilterImpl::get_algorithm_heuristic(
         const TensorLayout& src, const TensorLayout& diff,
         const TensorLayout& grad, size_t workspace_limit_in_bytes,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     auto fm = check_layout_fwd(src, grad, diff);
     return get_algorithm_heuristic(src, diff, fm, workspace_limit_in_bytes,
-                                   reproducible);
+                                   attr);
 }
 
 ConvolutionBackwardFilterImpl::Algorithm*
 ConvolutionBackwardFilterImpl::get_algorithm_heuristic(
         const TensorLayout& src, const TensorLayout& diff,
         const CanonizedFilterMeta& grad, size_t workspace_limit_in_bytes,
-        bool reproducible) {
+        const AlgoAttribute& attr) {
     AlgoBase::SizeArgs args(this, src, diff, grad);
 
     if (is_miopen_supported(args.as_fwd_args())) {
         auto algo =
-                megdnn::get_reproducible_algo<ConvolutionBackwardFilterImpl>(
-                        sm_algo_pack.miopen_algos[0], reproducible);
+                megdnn::get_algo_with_attribute<ConvolutionBackwardFilterImpl>(
+                        sm_algo_pack.miopen_algos[0], attr);
         if (algo)
             return algo;
     }
 
     if (args.grad_filter_meta.group > 1 &&
-        sm_algo_pack.chanwise.is_available_reproducible(
-                args, reproducible, workspace_limit_in_bytes)) {
+        sm_algo_pack.chanwise.is_available_attribute(
+                args, attr, workspace_limit_in_bytes)) {
         // prefer special chanwise impl
         return &sm_algo_pack.chanwise;
     }
 
-    if (reproducible) {
-        return megdnn::get_reproducible_algo<ConvolutionBackwardFilterImpl>(
+    if (attr != AlgoAttribute::DEFAULT) {
+        return megdnn::get_algo_with_attribute<ConvolutionBackwardFilterImpl>(
                 sm_algo_pack.non_miopen_algos, args, workspace_limit_in_bytes,
-                "rocm conv bwd_filter");
+                "rocm conv bwd_filter", attr);
     } else {
         return megdnn::get_usable_algo<ConvolutionBackwardFilterImpl>(
                 sm_algo_pack.non_miopen_algos, args, workspace_limit_in_bytes,

dnn/src/rocm/convolution/opr_impl.h

@@ -26,9 +26,9 @@ public:
     AlgorithmInfo get_algorithm_info_heuristic(
             const TensorLayout& src, const CanonizedFilterMeta& filter,
             const TensorLayout& dst, size_t workspace_limit_in_bytes,
-            bool reproducible) {
+            const AlgoAttribute& attr) {
         return get_algorithm_heuristic(src, filter, dst,
-                                       workspace_limit_in_bytes, reproducible)
+                                       workspace_limit_in_bytes, attr)
                 ->info();
     }
     size_t get_workspace_in_bytes(const TensorLayout& src,
@@ -76,12 +76,12 @@ private:
                                        const TensorLayout& filter,
                                        const TensorLayout& dst,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
+                                       const AlgoAttribute& attr) override;
     Algorithm* get_algorithm_heuristic(const TensorLayout& src,
                                        const CanonizedFilterMeta& filter,
                                        const TensorLayout& dst,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible);
+                                       const AlgoAttribute& attr);
 
     static AlgoPack sm_algo_pack;
 };
@@ -94,9 +94,9 @@ public:
     AlgorithmInfo get_algorithm_info_heuristic(
             const CanonizedFilterMeta& filter, const TensorLayout& diff,
             const TensorLayout& grad, size_t workspace_limit_in_bytes,
-            bool reproducible) {
+            const AlgoAttribute& attr) {
         return get_algorithm_heuristic(filter, diff, grad,
-                                       workspace_limit_in_bytes, reproducible)
+                                       workspace_limit_in_bytes, attr)
                 ->info();
     }
     size_t get_workspace_in_bytes(const TensorLayout& filter,
@@ -122,12 +122,12 @@ private:
                                        const TensorLayout& diff,
                                        const TensorLayout& grad,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
+                                       const AlgoAttribute& attr) override;
     Algorithm* get_algorithm_heuristic(const CanonizedFilterMeta& filter,
                                        const TensorLayout& diff,
                                        const TensorLayout& grad,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible);
+                                       const AlgoAttribute& attr);
 
     static AlgoPack sm_algo_pack;
 };
@@ -141,9 +141,9 @@ public:
                                                const TensorLayout& diff,
                                                const CanonizedFilterMeta& grad,
                                                size_t workspace_limit_in_bytes,
-                                               bool reproducible) {
+                                               const AlgoAttribute& attr) {
         return get_algorithm_heuristic(src, diff, grad,
-                                       workspace_limit_in_bytes, reproducible)
+                                       workspace_limit_in_bytes, attr)
                 ->info();
     }
     size_t get_workspace_in_bytes(const TensorLayout& src,
@@ -169,12 +169,12 @@ private:
                                        const TensorLayout& diff,
                                        const TensorLayout& grad,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible) override;
+                                       const AlgoAttribute& attr) override;
     Algorithm* get_algorithm_heuristic(const TensorLayout& src,
                                        const TensorLayout& diff,
                                        const CanonizedFilterMeta& grad,
                                        size_t workspace_limit_in_bytes,
-                                       bool reproducible);
+                                       const AlgoAttribute& attr);
 
     static AlgoPack sm_algo_pack;
 };

dnn/src/rocm/matrix_mul/algos.h

@@ -70,12 +70,11 @@ public:
     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,
+    bool is_available_attribute(
+            const SizeArgs& args,
+            const AlgoAttribute& attr = AlgoAttribute::REPRODUCIBLE,
             size_t limit = std::numeric_limits<size_t>::max()) const {
-        return (!reproducible ||
-                contain_attribute(AlgoAttribute::REPRODUCIBLE)) &&
-               is_available_wk(args, limit);
+        return contain_attribute(attr) && is_available_wk(args, limit);
     }
     AlgoBase& check_workspace(const SizeArgs& args,
                               const Workspace& workspace) {

dnn/src/rocm/matrix_mul/opr_impl.cpp

@@ -29,16 +29,16 @@ MatrixMulForwardImpl::get_all_algorithms(const TensorLayout& A,
 
 MatrixMulForwardImpl::Algorithm* MatrixMulForwardImpl::get_algorithm_heuristic(
         const TensorLayout& A, const TensorLayout& B, const TensorLayout& C,
-        size_t workspace_limit_in_bytes, bool reproducible) {
+        size_t workspace_limit_in_bytes, const AlgoAttribute& attr) {
     AlgoBase::SizeArgs args{this, A, B, C};
-    if (sm_algo_pack.blas.is_available_reproducible(
-                args, reproducible, workspace_limit_in_bytes)) {
+    if (sm_algo_pack.blas.is_available_attribute(args, attr,
+                                                 workspace_limit_in_bytes)) {
         return &sm_algo_pack.blas;
     }
-    if (reproducible) {
-        return megdnn::get_reproducible_algo<MatrixMulForwardImpl>(
+    if (attr != AlgoAttribute::DEFAULT) {
+        return megdnn::get_algo_with_attribute<MatrixMulForwardImpl>(
                 sm_algo_pack.all_algos, args, workspace_limit_in_bytes,
-                "matrix mul forward");
+                "matrix mul forward", attr);
     } else {
         return megdnn::get_usable_algo<MatrixMulForwardImpl>(
                 sm_algo_pack.all_algos, args, workspace_limit_in_bytes,

dnn/src/rocm/matrix_mul/opr_impl.h

@@ -40,7 +40,7 @@ private:
                                        const TensorLayout& /*B*/,
                                        const TensorLayout& /*C*/,
                                        size_t /*workspace_limit_in_bytes*/,
-                                       bool /*reproducible*/) override;
+                                       const AlgoAttribute& /*attr*/) override;
 
     const char* get_algorithm_set_name() const override {
         return "ROCM MATMUL";

src/opr/impl/search_policy/algo_chooser.cpp

@@ -278,6 +278,15 @@ std::vector<megdnn::Algorithm::SearchItem> flatten_search_space(
     return ret;
 }
 
+AlgoAttribute extract_algo_attribute_from_execution_strategy(
+        const ExecutionStrategy& strategy) {
+    AlgoAttribute ret = AlgoAttribute::DEFAULT;
+    if (strategy & ExecutionStrategy::REPRODUCIBLE) {
+        ret |= AlgoAttribute::REPRODUCIBLE;
+    }
+    return ret;
+}
+
 //! Test whether the algo attribute of a algo match the require
 //! algo_strategy
 static bool algo_attribute_match_strategy(AlgoAttribute attribute,
@@ -290,7 +299,6 @@ static bool algo_attribute_match_strategy(AlgoAttribute attribute,
     }
     return ret;
 }
-
 }  // namespace
 
 namespace mgb {
@@ -303,9 +311,9 @@ void AlgoChooser<Opr>::profile(ExeContext& ctx,
         return;
     AlgoChooserProfileCache::Result prof_rst;
 
-    std::string str_on_inp_shape = ssprintf(
-            "on input layouts (%s, %s)", ctx.layouts()[0].to_string().c_str(),
-            ctx.layouts()[1].to_string().c_str());
+    auto target_attribute =
+            extract_algo_attribute_from_execution_strategy(selected_strategy);
+    std::string layouts_str = format_fixlayouts<Opr>(ctx.layouts(), arity_in, arity_out);
     double cur_timeout = 0;
 
     auto workspace_limit = WorkspaceLimitGetter::get_workspace_limit(
@@ -316,20 +324,22 @@ void AlgoChooser<Opr>::profile(ExeContext& ctx,
         Maybe<AlgoChooserProfileCache::ResultEntry> cur_rst;
         std::string msg = ssprintf("profiling %s algorithm %s %s",
                                    ctx.mgb_opr()->dyn_typeinfo()->name,
-                                   algo.name.c_str(), str_on_inp_shape.c_str());
+                                   algo.name.c_str(), layouts_str.c_str());
         ImplExecutionPolicy policy;
         policy.algo = algo.desc;
         ctx.construct_execution_policy(selected_strategy, policy);
         if (ctx.get_workspace_size_bytes(policy) >= workspace_limit) {
             continue;
         }
-        auto algo_attribute = ctx.megdnn_opr()
-                                      ->get_algorithm_from_desc(policy.algo)
-                                      ->attribute();
-        if (!algo_attribute_match_strategy(algo_attribute, selected_strategy)) {
+        auto palgo = ctx.megdnn_opr()->get_algorithm_from_desc(policy.algo);
+        if (!algo_attribute_match_strategy(palgo->attribute(),
+                                           selected_strategy)) {
             mgb_log_debug(
-                    "skip algo %s, which is not match the profile strategy.",
-                    algo.name.c_str());
+                    "skip algo %s with attribute%s, which is not match the "
+                    "profile strategy required attribute%s.",
+                    algo.name.c_str(),
+                    Algorithm::attribute_str(palgo->attribute()).c_str(),
+                    Algorithm::attribute_str(target_attribute).c_str());
             continue;
         }
 
@@ -360,9 +370,10 @@ void AlgoChooser<Opr>::profile(ExeContext& ctx,
                       rst.workspace, rst.time);
         prof_rst.push_back(rst);
     }
-    std::string msg = ssprintf("no usable %s algorithm %s",
-                                ctx.mgb_opr()->dyn_typeinfo()->name,
-                                str_on_inp_shape.c_str());
+    std::string msg =
+            ssprintf("no usable %s algorithm %s with attribute(%s)",
+                     ctx.mgb_opr()->dyn_typeinfo()->name, layouts_str.c_str(),
+                     Algorithm::attribute_str(target_attribute).c_str());
     mgb_assert(!prof_rst.empty(), "%s", msg.c_str());
 
     FixedTensorLayouts origin_layouts = ctx.layouts();
@@ -589,14 +600,15 @@ AlgoChooser<Opr>::ExeContext::choose_by_heuristic(
                 "workspace_limit should not be setted if choose algo by "
                 "heuristic");
     }
-    bool reproducible = static_cast<bool>(selected_strategy &
-                                          ExecutionStrategy::REPRODUCIBLE);
     auto workspace_limit = WorkspaceLimitGetter::get_workspace_limit(
             owner_graph(), m_cn, m_execution_policy.workspace_limit);
     ImplExecutionPolicy policy;
     policy.algo = APPLY(m_megdnn_opr->get_algorithm_info_heuristic(
-                                args..., workspace_limit, reproducible),
-                        m_layouts).desc;
+                                args..., workspace_limit,
+                                extract_algo_attribute_from_execution_strategy(
+                                        selected_strategy)),
+                        m_layouts)
+                          .desc;
 
     Algorithm* algo = m_megdnn_opr->get_algorithm_from_desc(policy.algo);
     mgb_assert(algo, "Unknown algo description");
@@ -647,8 +659,6 @@ void AlgoChooser<Opr>::ExeContext::construct_execution_policy(
         ExecutionStrategy selected_strategy,
         typename AlgoChooser<Opr>::ImplExecutionPolicy& policy,
         bool retrive_from_cache) const {
-    bool reproducible = static_cast<bool>(selected_strategy &
-                                          ExecutionStrategy::REPRODUCIBLE);
     if (!policy.algo.valid()) {
         if (retrive_from_cache) {
             policy.algo =
@@ -656,11 +666,13 @@ void AlgoChooser<Opr>::ExeContext::construct_execution_policy(
         } else {
             auto workspace_limit = WorkspaceLimitGetter::get_workspace_limit(
                     owner_graph(), m_cn, m_execution_policy.workspace_limit);
-            policy.algo = APPLY(m_megdnn_opr->get_algorithm_info_heuristic(
-                                        args..., workspace_limit,
-                                        reproducible),
-                                m_layouts)
-                                  .desc;
+            policy.algo =
+                    APPLY(m_megdnn_opr->get_algorithm_info_heuristic(
+                                  args..., workspace_limit,
+                                  extract_algo_attribute_from_execution_strategy(
+                                          selected_strategy)),
+                          m_layouts)
+                            .desc;
         }
         mgb_assert(policy.algo.valid(),
                    "No algo found from cache or heuristic, maybe some error "

src/opr/test/dnn/convolution.cpp

@@ -2375,7 +2375,7 @@ public:
                  AlgorithmInfo(const TensorLayout& p0, const TensorLayout& p1,
                             const TensorLayout& p2,
                             size_t workspace_limit_in_bytes,
-                            bool reproducible));
+                            const AlgoAttribute& attr));
 
     MOCK_METHOD3(get_all_algorithms,
                  std::vector<Algorithm*>(const TensorLayout& p0,
@@ -2385,7 +2385,7 @@ public:
                  Algorithm*(const TensorLayout& p0, const TensorLayout& p1,
                             const TensorLayout& p2,
                             size_t workspace_limit_in_bytes,
-                            bool reproducible));
+                            const AlgoAttribute& attr));
 
     MOCK_METHOD1(get_algorithm_from_desc,
                  Algorithm*(const AlgorithmDesc&));