refactor(mgb): move profile cache out of mgb opr and update CACHE_KEY_VERSION

GitOrigin-RevId: db433164d297c9d6438fc45203e57e72df3f9b0b

src/opr/impl/dnn/convolution.cpp

@@ -36,8 +36,6 @@ using namespace opr;
 using namespace cg::static_infer;
 using intl::WorkspaceLimitGetter;
 
-#define CACHE_KEY_VERSION "v2"
-
 /* ==================== misc impl  ==================== */
 
 mixin::Convolution::~Convolution() = default;
@@ -103,26 +101,12 @@ void mixin::Convolution::init_output_static_infer_desc_for_bwd_data(
                              {SourceType::DEP, inp_deps, infer_wk});
 }
 
-#define IMPL_CONV(_cls, _prof_name)                                  \
-    void _cls::init_profile_cache() {                                \
-        std::string name(_prof_name CACHE_KEY_VERSION);              \
-        name.append(megdnn_opr()->get_algorithm_set_name());         \
-        m_profile_cache = std::make_unique<AlgoChooserProfileCache>( \
-                comp_node(), name.c_str());                          \
-    }                                                                \
-    std::pair<const void*, size_t> _cls::param_blob() const {        \
-        return {&param(), sizeof(Param)};                            \
-    }                                                                \
+#define IMPL_CONV(_cls)                                       \
+    std::pair<const void*, size_t> _cls::param_blob() const { \
+        return {&param(), sizeof(Param)};                     \
+    }                                                         \
     MGB_DYN_TYPE_OBJ_FINAL_IMPL(_cls)
 
-AlgoChooserProfileCache& mixin::Convolution::profile_cache() const {
-    if (!m_profile_cache) {
-        const_cast<Convolution*>(this)->init_profile_cache();
-        mgb_assert(m_profile_cache);
-    }
-    return *m_profile_cache;
-}
-
 class mixin::WeightPreprocessExecutor::PreprocessedFilterExecDep final
         : public cg::GraphExecutable::ExecDependency {
     std::unique_ptr<PreprocessedFilter> m_pf;
@@ -209,7 +193,7 @@ bool mixin::WeightPreprocessExecutor::mixin_allow_weight_preprocess(
 
 /* ==================== ConvolutionForward  ==================== */
 
-IMPL_CONV(ConvolutionForward, "conv_fwd");
+IMPL_CONV(ConvolutionForward);
 
 ConvolutionForward::ConvolutionForward(VarNode* src, VarNode* filter,
                                        const Param& param,
@@ -335,7 +319,7 @@ void ConvolutionForward::scn_do_execute_preprocess() {
 }
 
 /* ==================== ConvolutionBackwardData  ==================== */
-IMPL_CONV(ConvolutionBackwardData, "conv_bwd_data");
+IMPL_CONV(ConvolutionBackwardData);
 
 ConvolutionBackwardData::ConvolutionBackwardData(
         VarNode* filter, VarNode* diff, VarNode* src_for_shp,
@@ -426,7 +410,7 @@ MGB_IMPL_OPR_GRAD(ConvolutionBackwardData) {
 #endif
 
 /* ==================== ConvolutionBackwardFilter  ==================== */
-IMPL_CONV(ConvolutionBackwardFilter, "conv_bwd_filter");
+IMPL_CONV(ConvolutionBackwardFilter);
 
 ConvolutionBackwardFilter::ConvolutionBackwardFilter(
         VarNode* src, VarNode* diff, VarNode* filter, const Param& param,
@@ -480,7 +464,7 @@ MGB_IMPL_OPR_GRAD(ConvolutionBackwardFilter) {
 #endif
 /* ==================== Convolution3DForward ==================== */
 
-IMPL_CONV(Convolution3DForward, "conv3d_fwd");
+IMPL_CONV(Convolution3DForward);
 
 Convolution3DForward::Convolution3DForward(VarNode* src, VarNode* filter,
                                            const Param& param,
@@ -553,7 +537,7 @@ size_t Convolution3DForward::get_workspace_size_bytes(
 }
 
 /* ==================== Convolution3DBackwardData  ==================== */
-IMPL_CONV(Convolution3DBackwardData, "conv3d_bwd_data");
+IMPL_CONV(Convolution3DBackwardData);
 
 Convolution3DBackwardData::Convolution3DBackwardData(
         VarNode* filter, VarNode* diff, VarNode* src_for_shp,
@@ -631,7 +615,7 @@ MGB_IMPL_OPR_GRAD(Convolution3DBackwardData) {
 #endif
 
 /* ==================== Convolution3DBackwardFilter  ==================== */
-IMPL_CONV(Convolution3DBackwardFilter, "conv3d_bwd_filter");
+IMPL_CONV(Convolution3DBackwardFilter);
 
 Convolution3DBackwardFilter::Convolution3DBackwardFilter(
         VarNode* src, VarNode* diff, VarNode* filter, const Param& param,
@@ -719,7 +703,7 @@ SymbolVar MaskPropagate::make(SymbolVar src, const Param& param,
 }
 
 /* ==================== ConvBiasForward  ==================== */
-IMPL_CONV(ConvBiasForward, "conv_bias_fwd");
+IMPL_CONV(ConvBiasForward);
 
 ConvBiasForward::ConvBiasForward(VarNode* src, VarNode* filter,
                                  const Param& param,
@@ -1005,7 +989,7 @@ void ConvBiasForward::scn_do_execute_preprocess() {
 
 /* ===================== LocalShareForward ==================== */
 
-IMPL_CONV(LocalShareForward, "local_share");
+IMPL_CONV(LocalShareForward);
 
 LocalShareForward::LocalShareForward(VarNode* src, VarNode* filter,
                                      const Param& param,
@@ -1073,7 +1057,7 @@ MGB_IMPL_OPR_GRAD(LocalShareForward) {
 
 /* ===================== LocalShareBackwardData ==================== */
 
-IMPL_CONV(LocalShareBackwardData, "local_share_bwd_data");
+IMPL_CONV(LocalShareBackwardData);
 
 LocalShareBackwardData::LocalShareBackwardData(VarNode* filter, VarNode* diff,
                                                VarNode* src_for_shp,
@@ -1153,7 +1137,7 @@ MGB_IMPL_OPR_GRAD(LocalShareBackwardData) {
 
 /* ==================== LocalShareBackwardFilter  ==================== */
 
-IMPL_CONV(LocalShareBackwardFilter, "local_share_bwd_filter");
+IMPL_CONV(LocalShareBackwardFilter);
 
 LocalShareBackwardFilter::LocalShareBackwardFilter(
         VarNode* src, VarNode* diff, VarNode* filter, const Param& param,
@@ -1208,7 +1192,7 @@ MGB_IMPL_OPR_GRAD(LocalShareBackwardFilter) {
 
 /* ===================== DeformableConvForward ==================== */
 
-IMPL_CONV(DeformableConvForward, "deformable_conv");
+IMPL_CONV(DeformableConvForward);
 
 DeformableConvForward::DeformableConvForward(VarNode* src, VarNode* filter,
                                              VarNode* offset, VarNode* mask,
@@ -1293,7 +1277,7 @@ MGB_IMPL_OPR_GRAD(DeformableConvForward) {
 
 /* ==================== DeformableConvBackwardData  ==================== */
 
-IMPL_CONV(DeformableConvBackwardData, "deformalbe_conv_backward_data");
+IMPL_CONV(DeformableConvBackwardData);
 
 DeformableConvBackwardData::DeformableConvBackwardData(
         VarNode* src, VarNode* filter, VarNode* offset, VarNode* mask,
@@ -1425,7 +1409,7 @@ void DeformableConvBackwardData::init_output_static_infer_desc() {
 
 /* ==================== DeformableConvBackwardFilter  ==================== */
 
-IMPL_CONV(DeformableConvBackwardFilter, "deformalbe_conv_backward_filter");
+IMPL_CONV(DeformableConvBackwardFilter);
 
 DeformableConvBackwardFilter::DeformableConvBackwardFilter(
         VarNode* src, VarNode* filter, VarNode* offset, VarNode* mask,
@@ -1484,7 +1468,7 @@ size_t DeformableConvBackwardFilter::get_workspace_size_bytes(
 }
 
 /* ==================== BatchConvBiasForward  ==================== */
-IMPL_CONV(BatchConvBiasForward, "batch_conv_bias_fwd");
+IMPL_CONV(BatchConvBiasForward);
 
 BatchConvBiasForward::BatchConvBiasForward(VarNode* src, VarNode* filter,
                                            const Param& param,

src/opr/impl/search_policy/algo_chooser.cpp

@@ -36,15 +36,29 @@ using mgb::opr::intl::WorkspaceLimitGetter;
 // timeout delta to be added with fastest known algorithm for new algos
 constexpr double TIMEOUT_TOLERANCE = 2;
 
+#define CACHE_KEY_VERSION "v3"
+
+namespace {
+template <typename Opr>
+std::string profile_name(Opr* opr) {
+    std::string ret =
+            std::string(MegDNNOpr2MGBOpr<Opr>::MGBOpr::typeinfo()->name) +
+            CACHE_KEY_VERSION;
+    ret.append(opr->get_algorithm_set_name());
+    return ret;
+}
+}
+
 namespace mgb {
 namespace opr {
 
 template <typename Opr>
 AlgoChooserProfileCache::Result AlgoChooser<Opr>::get_profile_result(
         ExeContext& ctx, bool enable_update) {
-    AlgoChooserProfileCache& cache = ctx.mgb_opr()->profile_cache();
+    AlgoChooserProfileCache cache(ctx.mgb_opr()->comp_node(),
+                                  profile_name(ctx.megdnn_opr()).c_str());
 
-    ConvTensorLayouts origin_layouts = ctx.layouts();
+    TensorLayoutArray origin_layouts = ctx.layouts();
     typename Opr::Param origin_param = ctx.mgb_opr()->param();
     AlgoChooserProfileCache::Key cache_key{origin_layouts.data(),
                                            origin_layouts.size(), &origin_param,
@@ -131,12 +145,12 @@ typename AlgoChooser<Opr>::ImplAlgo AlgoChooser<Opr>::choose_by_profile(
                        "profiling result but not in algo_map; please "
                        "report this "
                        "bug; opr: %s{%s}, shapes: %s %s %s",
+                       i.algo.c_str(),
                        ctx.mgb_opr()->cname(),
                        ctx.mgb_opr()->dyn_typeinfo()->name,
                        ctx.layouts()[0].TensorShape::to_string().c_str(),
                        ctx.layouts()[1].TensorShape::to_string().c_str(),
-                       ctx.layouts()[2].TensorShape::to_string().c_str(),
-                       i.algo.c_str());
+                       ctx.layouts()[2].TensorShape::to_string().c_str());
             return iter->second;
         }
     }
@@ -153,7 +167,7 @@ typename AlgoChooser<Opr>::ImplAlgo AlgoChooser<Opr>::choose_by_profile(
 }
 
 template <typename Opr>
-size_t AlgoChooser<Opr>::setup_algo(const ConvTensorLayouts& layouts,
+size_t AlgoChooser<Opr>::setup_algo(const TensorLayoutArray& layouts,
                                     Opr* megdnn_opr, const MGBOpr* mgb_opr,
                                     bool allow_weight_preprocess) {
     if (WorkspaceLimitGetter::is_prealloc_run(mgb_opr->owner_graph())) {
@@ -220,7 +234,7 @@ typename AlgoChooser<Opr>::ImplAlgo AlgoChooser<Opr>::get_algo(
     AlgoChooser<megdnn::Opr>::choose_by_profile(                             \
             ExeContext& ctx, bool require_reproducible, bool enable_update); \
     template size_t AlgoChooser<megdnn::Opr>::setup_algo(                    \
-            const ConvTensorLayouts& layouts, megdnn::Opr* megdnn_opr,       \
+            const TensorLayoutArray& layouts, megdnn::Opr* megdnn_opr,       \
             const MGBOpr* mgb_opr, bool allow_weight_preprocess);
 
 MGB_FOREACH_FASTRUN_OPR(INST)

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

@@ -74,12 +74,8 @@ class Convolution {
         mutable bool m_policy_accessed = false;
         ExecutionPolicy m_policy;
 
-        std::unique_ptr<AlgoChooserProfileCache> m_profile_cache;
-
         AlgoChooserHook m_algo_chooser;
 
-        virtual void init_profile_cache() = 0;
-
         //! init output desc for conv backward data oprs; it handles both grad
         //! usage and deconv usage
         template <class MgbOpr, class MegDNNOpr>
@@ -159,7 +155,6 @@ class ConvolutionTestingPeer;
 MGB_DEFINE_OPR_CLASS(ConvolutionForward,
         intl::ConvolutionForwardBase, public mixin::Convolution) // {
 
-    void init_profile_cache() override;
     void init_output_dtype() override;
     size_t get_workspace_size_bytes(
             const TensorShapeArray &input_shapes,
@@ -245,7 +240,6 @@ public:
                           const ExecutionPolicy& policy = {},
                           const OperatorNodeConfig& config = {});
 
-    void init_profile_cache() override;
     std::pair<const void*, size_t> param_blob() const override;
 
     static void check_winograd_param_valid(
@@ -268,7 +262,6 @@ MGB_DEFINE_OPR_CLASS(ConvolutionBackwardData,
     void init_output_format() override;
 
     void add_input_layout_constraint() override;
-    void init_profile_cache() override;
 
     void scn_do_execute() override;
     NodeProp *do_make_node_prop() const override;
@@ -310,7 +303,6 @@ MGB_DEFINE_OPR_CLASS(ConvolutionBackwardFilter,
         intl::MegDNNOprWrapperBwd<megdnn::ConvolutionBackwardFilter>,
         public mixin::Convolution ) // {
 
-    void init_profile_cache() override final;
 
     size_t get_workspace_size_bytes(
             const TensorShapeArray &input_shapes,
@@ -360,7 +352,6 @@ MGB_DEFINE_OPR_CLASS(Convolution3DForward,
         intl::MegDNNOprWrapperFwd<megdnn::Convolution3DForward>,
         public mixin::Convolution) // {
 
-    void init_profile_cache() override;
     void init_output_dtype() override;
     size_t get_workspace_size_bytes(
             const TensorShapeArray &input_shapes,
@@ -391,7 +382,6 @@ MGB_DEFINE_OPR_CLASS(Convolution3DBackwardData,
     void init_output_static_infer_desc() override;
 
     void add_input_layout_constraint() override;
-    void init_profile_cache() override;
 
     void scn_do_execute() override;
     NodeProp *do_make_node_prop() const override;
@@ -433,8 +423,6 @@ MGB_DEFINE_OPR_CLASS(Convolution3DBackwardFilter,
         intl::MegDNNOprWrapperBwd<megdnn::Convolution3DBackwardFilter>,
         public mixin::Convolution) // {
 
-    void init_profile_cache() override final;
-
     size_t get_workspace_size_bytes(
             const TensorShapeArray &input_shapes,
             const TensorShapeArray &output_shapes) const override final;
@@ -455,7 +443,6 @@ MGB_DEFINE_OPR_CLASS(Convolution3DBackwardFilter,
 MGB_DEFINE_OPR_CLASS(LocalShareForward,
                      intl::MegDNNOprWrapperFwd<megdnn::LocalShareForward>,
                      public mixin::Convolution)  // {
-    void init_profile_cache() override final;
     void init_output_dtype() override;
     void init_output_format() override;
 
@@ -483,7 +470,6 @@ MGB_DEFINE_OPR_CLASS(
     void init_output_dtype() override;
 
     void add_input_layout_constraint() override;
-    void init_profile_cache() override;
 
     void scn_do_execute() override;
     NodeProp* do_make_node_prop() const override;
@@ -506,7 +492,6 @@ MGB_DEFINE_OPR_CLASS(
         LocalShareBackwardFilter,
         intl::MegDNNOprWrapperBwd<megdnn::LocalShareBackwardFilter>,
         public mixin::Convolution) // {
-    void init_profile_cache() override final;
 
     size_t get_workspace_size_bytes(
             const TensorShapeArray& input_shapes,
@@ -542,7 +527,6 @@ MGB_DEFINE_OPR_CLASS(DeformableConvForward,
 
         std::pair<const void*, size_t> param_blob() const override;
     private:
-        void init_profile_cache() override;
         void init_output_dtype() override;
         void init_output_format() override;
         size_t get_workspace_size_bytes(
@@ -589,7 +573,6 @@ private:
     void add_input_layout_constraint() override {
         mixin::megdnn_utils::add_input_layout_constraint_contig(*this);
     }
-    void init_profile_cache() override;
 };
 
 MGB_DEFINE_OPR_CLASS(
@@ -612,7 +595,6 @@ public:
     std::pair<const void*, size_t> param_blob() const override;
 
 private:
-    void init_profile_cache() override;
     size_t get_workspace_size_bytes(const TensorShapeArray& input_shapes,
                                     const TensorShapeArray& output_shapes)
             const override final;
@@ -668,7 +650,6 @@ public:
                           const ExecutionPolicy& policy = {},
                           const OperatorNodeConfig& config = {});
 
-    void init_profile_cache() override;
     std::pair<const void*, size_t> param_blob() const override;
 };
 using BatchConvBias = BatchConvBiasForward;

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

@@ -48,16 +48,16 @@ class AlgoChooser {
 
     using ImplAlgo = typename Opr::AlgorithmInfo;
     using MGBOpr = typename MegDNNOpr2MGBOpr<Opr>::MGBOpr;
-    using ConvTensorLayouts = std::array<TensorLayout, arity>;
+    using TensorLayoutArray = std::array<TensorLayout, arity>;
 
     class ExeContext {
-        const ConvTensorLayouts& m_layouts;
+        const TensorLayoutArray& m_layouts;
         Opr* m_megdnn_opr;
         const MGBOpr* m_mgb_opr;
         bool m_allow_weight_preprocess;
 
     public:
-        ExeContext(const ConvTensorLayouts& layouts, Opr* megdnn_opr,
+        ExeContext(const TensorLayoutArray& layouts, Opr* megdnn_opr,
                    const MGBOpr* mgb_opr, bool allow_weight_preprocess)
                 : m_layouts{layouts},
                   m_megdnn_opr{megdnn_opr},
@@ -65,9 +65,9 @@ class AlgoChooser {
                   m_allow_weight_preprocess{allow_weight_preprocess} {
             mgb_assert(m_layouts.size() == layouts.size());
             static_assert(
-                    std::tuple_size<ConvTensorLayouts>::value == 3 ||
-                            std::tuple_size<ConvTensorLayouts>::value == 5 ||
-                            std::tuple_size<ConvTensorLayouts>::value == 8,
+                    std::tuple_size<TensorLayoutArray>::value == 3 ||
+                            std::tuple_size<TensorLayoutArray>::value == 5 ||
+                            std::tuple_size<TensorLayoutArray>::value == 8,
                     "Convolution AlgoChooser assumes arity = 3 , 5 or 8 (for "
                     "deformable conv)");
         }
@@ -80,7 +80,7 @@ class AlgoChooser {
             return m_layouts[idx];
         }
 
-        const ConvTensorLayouts& layouts() const { return m_layouts; }
+        const TensorLayoutArray& layouts() const { return m_layouts; }
 
         ImplAlgo choose_by_heuristic(bool reproducible = false) const;
 
@@ -125,7 +125,7 @@ public:
     /*!
      * \brief setup algorithm and return workspace size
      */
-    static size_t setup_algo(const ConvTensorLayouts& layouts, Opr* megdnn_opr,
+    static size_t setup_algo(const TensorLayoutArray& layouts, Opr* megdnn_opr,
                              const MGBOpr* mgb_opr,
                              bool allow_weight_preprocess = false);
 };