fix(mgb): fix loss execution policy after opr shallow copy

GitOrigin-RevId: 4738136e4a8a3270a2b5343483539f46afe05c6d

imperative/src/impl/ops/specializations.cpp

@@ -183,7 +183,7 @@ namespace pooling {
 auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& pool = static_cast<const Pooling&>(def);
     OperatorNodeConfig config{pool.make_name()};
-    return opr::Pooling::make(inputs[0], pool.param(), config);
+    return opr::Pooling::make(inputs[0], pool.param(), pool.policy(), config);
 }
 OP_TRAIT_REG(Pooling, Pooling).apply_on_var_node(apply_on_var_node).fallback();
 }  // namespace pooling

src/core/include/megbrain/ir/ops.td

@@ -63,7 +63,7 @@ def DeformableConv : MgbHashableOp<"DeformableConv", [ConvolutionParam, Executio
 
 def GroupLocal: MgbHashableOp<"GroupLocal", [ConvolutionParam]>;
 
-def Pooling: MgbHashableOp<"Pooling", [PoolingParam]>;
+def Pooling: MgbHashableOp<"Pooling", [PoolingParam, ExecutionPolicyParamBase<"policy">]>;
 
 def AdaptivePooling : MgbHashableOp<"AdaptivePooling", [AdaptivePoolingParam]>;
 

src/gopt/impl/global_layout_transform/opr_format_modifier.cpp

@@ -31,7 +31,21 @@ using namespace opr;
 
 namespace {
 template <class MegDNNConv = megdnn::Convolution>
-struct MakeConvCaller2 {
+struct MakeOprWithPolicyCaller1 {
+    template <typename Opr>
+    static VarNode* make(
+            const cg::VarNodeArray& inputs, const typename MegDNNConv::Param& param,
+            const megdnn::param::ExecutionPolicy& execution_policy,
+            const OperatorNodeConfig& config) {
+        if (inputs.size() == 1) {
+            return Opr::make(inputs[0], param, execution_policy, config).node();
+        }
+        return nullptr;
+    }
+};
+
+template <class MegDNNConv = megdnn::Convolution>
+struct MakeOprWithPolicyCaller2 {
     template <typename Opr>
     static VarNode* make(
             const cg::VarNodeArray& inputs, const typename MegDNNConv::Param& param,
@@ -46,7 +60,7 @@ struct MakeConvCaller2 {
 };
 
 template <class MegDNNConv = megdnn::Convolution>
-struct MakeConvCaller3 {
+struct MakeOprWithPolicyCaller3 {
     template <typename Opr>
     static VarNode* make(
             const cg::VarNodeArray& inputs, const typename MegDNNConv::Param& param,
@@ -63,7 +77,7 @@ struct MakeConvCaller3 {
 };
 
 template <class MegDNNConv = megdnn::Convolution>
-struct MakeConvCaller4 {
+struct MakeOprWithPolicyCaller4 {
     template <typename Opr>
     static VarNode* make(
             const cg::VarNodeArray& inputs, const typename MegDNNConv::Param& param,
@@ -80,7 +94,7 @@ struct MakeConvCaller4 {
 };
 
 template <class MegDNNConv = megdnn::Convolution>
-struct MakeConvCaller5 {
+struct MakeOprWithPolicyCaller5 {
     template <typename Opr>
     static VarNode* make(
             const cg::VarNodeArray& inputs, const typename MegDNNConv::Param& param,
@@ -97,7 +111,7 @@ struct MakeConvCaller5 {
 };
 
 template <class MegDNNConv = megdnn::Convolution>
-struct MakeConvCallerEmpty {
+struct MakeOprWithPolicyCallerEmpty {
     template <typename Opr>
     static VarNode* make(
             const cg::VarNodeArray&, const typename MegDNNConv::Param&,
@@ -108,10 +122,10 @@ struct MakeConvCallerEmpty {
 
 template <
         class Opr, class Maker0, class MegDNNConv,
-        class Maker1 = MakeConvCallerEmpty<MegDNNConv>,
-        class Maker2 = MakeConvCallerEmpty<MegDNNConv>,
-        typename ConvParam = megdnn::param::Convolution>
-struct ConvMakerImpl {
+        class Maker1 = MakeOprWithPolicyCallerEmpty<MegDNNConv>,
+        class Maker2 = MakeOprWithPolicyCallerEmpty<MegDNNConv>,
+        typename ConvParam = typename MegDNNConv::Param>
+struct OprWithPolicyMakerImpl {
     static VarNode* make(
             const cg::VarNodeArray& inputs, const ConvParam& param,
             const megdnn::param::ExecutionPolicy& execution_policy,
@@ -130,33 +144,43 @@ struct ConvMakerImpl {
 };
 
 template <typename Opr>
-struct ConvMaker;
+struct OprWithPolicyMaker;
+
+template <>
+struct OprWithPolicyMaker<opr::Pooling>
+        : public OprWithPolicyMakerImpl<
+                  opr::Pooling, MakeOprWithPolicyCaller1<megdnn::Pooling>,
+                  megdnn::Pooling> {};
 
 template <>
-struct ConvMaker<opr::Convolution>
-        : public ConvMakerImpl<
-                  opr::Convolution, MakeConvCaller2<megdnn::Convolution>,
+struct OprWithPolicyMaker<opr::Convolution>
+        : public OprWithPolicyMakerImpl<
+                  opr::Convolution, MakeOprWithPolicyCaller2<megdnn::Convolution>,
                   megdnn::Convolution> {};
 template <>
-struct ConvMaker<opr::ConvolutionBackwardData>
-        : public ConvMakerImpl<
-                  opr::ConvolutionBackwardData, MakeConvCaller2<megdnn::Convolution>,
-                  megdnn::Convolution, MakeConvCaller3<megdnn::Convolution>> {};
+struct OprWithPolicyMaker<opr::ConvolutionBackwardData>
+        : public OprWithPolicyMakerImpl<
+                  opr::ConvolutionBackwardData,
+                  MakeOprWithPolicyCaller2<megdnn::Convolution>, megdnn::Convolution,
+                  MakeOprWithPolicyCaller3<megdnn::Convolution>> {};
 
 template <>
-struct ConvMaker<opr::ConvBiasForward>
-        : public ConvMakerImpl<
-                  opr::ConvBiasForward, MakeConvCaller2<megdnn::ConvBiasForward>,
-                  megdnn::ConvBiasForward, MakeConvCaller3<megdnn::ConvBiasForward>,
-                  MakeConvCaller4<megdnn::ConvBiasForward>, megdnn::param::ConvBias> {};
+struct OprWithPolicyMaker<opr::ConvBiasForward>
+        : public OprWithPolicyMakerImpl<
+                  opr::ConvBiasForward,
+                  MakeOprWithPolicyCaller2<megdnn::ConvBiasForward>,
+                  megdnn::ConvBiasForward,
+                  MakeOprWithPolicyCaller3<megdnn::ConvBiasForward>,
+                  MakeOprWithPolicyCaller4<megdnn::ConvBiasForward>,
+                  megdnn::param::ConvBias> {};
 template <>
-struct ConvMaker<opr::BatchConvBiasForward>
-        : public ConvMakerImpl<
+struct OprWithPolicyMaker<opr::BatchConvBiasForward>
+        : public OprWithPolicyMakerImpl<
                   opr::BatchConvBiasForward,
-                  MakeConvCaller2<megdnn::BatchConvBiasForward>,
+                  MakeOprWithPolicyCaller2<megdnn::BatchConvBiasForward>,
                   megdnn::BatchConvBiasForward,
-                  MakeConvCaller3<megdnn::BatchConvBiasForward>,
-                  MakeConvCaller4<megdnn::BatchConvBiasForward>,
+                  MakeOprWithPolicyCaller3<megdnn::BatchConvBiasForward>,
+                  MakeOprWithPolicyCaller4<megdnn::BatchConvBiasForward>,
                   megdnn::param::BatchConvBias> {};
 
 #include "../../opr/impl/internal/invoke.h"
@@ -254,7 +278,7 @@ struct OprFormatModifier;
             auto&& opr = opr_->cast_final_safe<_Opr>();              \
             auto param = opr.param();                                \
             param.format = opr_format;                               \
-            return ConvMaker<_Opr>::make(                            \
+            return OprWithPolicyMaker<_Opr>::make(                   \
                     i, param, opr.execution_policy(), opr.config()); \
             MIDOUT_E                                                 \
         }                                                            \
@@ -263,6 +287,7 @@ INST(Convolution);
 INST(ConvBiasForward);
 INST(ConvolutionBackwardData);
 INST(BatchConvBiasForward);
+INST(Pooling);
 #undef INST
 
 template <>
@@ -303,7 +328,6 @@ struct OprFormatModifier<WarpPerspective> {
             MIDOUT_E                                                     \
         }                                                                \
     };
-INST(PoolingForward, 1);
 INST(Resize, 2);
 #undef INST
 

src/gopt/impl/inference.cpp

@@ -1492,7 +1492,8 @@ std::unique_ptr<ConvertFormatPass> ConvertFormatPass::make_nhwcd4_converter() {
         }
         auto new_param = pooling_opr.param();
         new_param.format = megdnn::param::Pooling::Format::NHWCD4;
-        auto new_pooling_opr = opr::PoolingForward::make(inp, new_param, opr->config());
+        auto new_pooling_opr = opr::PoolingForward::make(
+                inp, new_param, pooling_opr.execution_policy(), opr->config());
         return new_pooling_opr.node()->owner_opr();
     };
 

src/gopt/impl/tensor_reformat.cpp

@@ -525,8 +525,8 @@ std::unique_ptr<EnableTensorCorePass> EnableTensorCorePass::
             }
             auto new_param = pooling.param();
             new_param.format = Format::NCHW32;
-            auto new_pooling =
-                    opr::PoolingForward::make(new_inp_var, new_param, opr->config());
+            auto new_pooling = opr::PoolingForward::make(
+                    new_inp_var, new_param, pooling.execution_policy(), opr->config());
             return new_pooling.node()->owner_opr();
         }
         return serialization::copy_opr_shallow(*opr, new_inp, opr->config());
@@ -795,8 +795,8 @@ std::unique_ptr<EnableCHWN4Pass> EnableCHWN4Pass::make_chwn4_converter() {
         if (varshape_changed.count(new_inp[0])) {
             auto new_param = pooling.param();
             new_param.format = Format::CHWN4;
-            auto new_pooling =
-                    opr::PoolingForward::make(new_inp[0], new_param, opr->config());
+            auto new_pooling = opr::PoolingForward::make(
+                    new_inp[0], new_param, pooling.execution_policy(), opr->config());
             varshape_changed.insert(new_pooling.node());
             return new_pooling.node()->owner_opr();
         }
@@ -1174,8 +1174,8 @@ std::unique_ptr<EnableNCHW4Pass> EnableNCHW4Pass::make_nchw4_converter() {
             mgb_assert(new_inp[0]->dtype().enumv() == DTypeEnum::QuantizedS8);
             auto new_param = pooling.param();
             new_param.format = Format::NCHW4;
-            auto new_pooling =
-                    opr::PoolingForward::make(new_inp[0], new_param, opr->config());
+            auto new_pooling = opr::PoolingForward::make(
+                    new_inp[0], new_param, pooling.execution_policy(), opr->config());
             mgb_assert(
                     new_pooling.shape().ndim == 5,
                     "out var of Pooling opr after transform must be 5 (got: "
@@ -1646,8 +1646,8 @@ void EnableNchwxxPass::fill_opr_convert_fun(size_t pack_c_size) {
         if (inp->shape().ndim == 5) {
             auto new_param = pooling_opr.param();
             new_param.format = pooling_format;
-            auto new_pooling_opr =
-                    opr::PoolingForward::make(inp, new_param, opr->config());
+            auto new_pooling_opr = opr::PoolingForward::make(
+                    inp, new_param, pooling_opr.execution_policy(), opr->config());
             mgb_assert(
                     new_pooling_opr.shape().ndim == 5,
                     "The pooling dst dim is not trans to nchwxx");
@@ -3003,7 +3003,8 @@ std::unique_ptr<EnableNCHW64Pass> EnableNCHW64Pass::make_nchw64_converter() {
             auto target_format = cur == Format::NCHW64 ? cur : Format::NHWC;
             auto param = pooling.param();
             param.format = target_format;
-            auto new_pool = opr::PoolingForward::make(inps[0], param, pooling.config());
+            auto new_pool = opr::PoolingForward::make(
+                    inps[0], param, pooling.execution_policy(), pooling.config());
             auto ret = new_pool.node()->owner_opr();
             format_map.insert(std::make_pair(ret, target_format));
             return ret;
@@ -3055,7 +3056,8 @@ std::unique_ptr<EnableNCHW64Pass> EnableNCHW64Pass::make_nchw64_converter() {
 
             auto param = pooling.param();
             param.format = out_format;
-            auto new_pool = opr::PoolingForward::make(inps[0], param, pooling.config());
+            auto new_pool = opr::PoolingForward::make(
+                    inps[0], param, pooling.execution_policy(), pooling.config());
             auto ret = new_pool.node()->owner_opr();
             format_map.insert(std::make_pair(ret, out_format));
             return ret;

src/gopt/test/layout_transform_pass.cpp

@@ -281,7 +281,7 @@ TEST(TestLayoutTransform, Resnet18_QS4) {
     auto new_out_var = new_output[0];
     /// check global layout transform pass
     auto nr_dimshuffle = find_opr_num<opr::Dimshuffle>(new_out_var);
-    ASSERT_EQ(nr_dimshuffle, 3u);
+    ASSERT_EQ(nr_dimshuffle, 5u);
     /// check pass fuse conv bias with z
     auto nr_elemwise_mult_type = find_opr_num<opr::ElemwiseMultiType>(new_out_var);
     ASSERT_EQ(nr_elemwise_mult_type, 4u);
@@ -822,7 +822,7 @@ TEST(TestLayoutTransform, Resnet18_F16) {
     auto new_out_var = new_output[0];
     /// check global layout transform pass
     auto nr_dimshuffle = find_opr_num<opr::Dimshuffle>(new_out_var);
-    ASSERT_EQ(nr_dimshuffle, 4u);
+    ASSERT_EQ(nr_dimshuffle, 2u);
     /// check pass fuse conv bias with z
     auto nr_elemwise = find_opr_num<opr::Elemwise>(new_out_var);
     ASSERT_EQ(nr_elemwise, 4u);

src/opr/impl/blas.sereg.h

@@ -80,14 +80,26 @@ struct OprLoadDumpImpl<opr::BatchedMatrixMul, 2>
                   opr::BatchedMatrixMul, MakeMatrixMulCaller<megdnn::BatchedMatrixMul>,
                   megdnn::BatchedMatrixMul> {};
 
+template <typename Opr>
+cg::OperatorNodeBase* opr_shallow_copy_matmul(
+        const serialization::OprShallowCopyContext& ctx,
+        const cg::OperatorNodeBase& opr_, const VarNodeArray& inputs,
+        const OperatorNodeConfig& config) {
+    MGB_MARK_USED_VAR(ctx);
+    auto&& opr = opr_.cast_final_safe<Opr>();
+    return OprLoadDumpImpl<Opr, 2>::make(
+                   inputs, opr.param(), opr.execution_policy_transient(), config)
+            ->owner_opr();
+}
+
 }  // namespace serialization
 
 namespace opr {
 
 using MatrixMulV2 = MatrixMul;
 using BatchedMatrixMulV2 = BatchedMatrixMul;
-MGB_SEREG_OPR(MatrixMulV2, 2);
-MGB_SEREG_OPR(BatchedMatrixMulV2, 2);
+MGB_SEREG_OPR_AND_REG_SHALLOW_COPY(MatrixMulV2, 2, opr_shallow_copy_matmul);
+MGB_SEREG_OPR_AND_REG_SHALLOW_COPY(BatchedMatrixMulV2, 2, opr_shallow_copy_matmul);
 MGB_SEREG_OPR(Dot, 2);
 MGB_SEREG_OPR(MatrixInverse, 1);
 MGB_SEREG_OPR(SVD, 1);

src/opr/impl/dnn/dnn.sereg.h

@@ -36,9 +36,10 @@ struct MakePoolingCaller1 {
     template <typename Opr>
     static VarNode* make(
             const cg::VarNodeArray& inputs, const typename MegDNNPooling::Param& param,
+            const megdnn::param::ExecutionPolicy& execution_policy,
             const OperatorNodeConfig& config) {
         if (inputs.size() == 1) {
-            return Opr::make(inputs[0], param, config).node();
+            return Opr::make(inputs[0], param, execution_policy, config).node();
         }
         return nullptr;
     }
@@ -78,9 +79,13 @@ struct MakePoolingBackwardCaller3 {
     template <typename Opr>
     static VarNode* make(
             const cg::VarNodeArray& inputs, const typename MegDNNPooling::Param& param,
+            const megdnn::param::ExecutionPolicy& execution_policy,
             const OperatorNodeConfig& config) {
         if (inputs.size() == 3) {
-            return Opr::make(inputs[0], inputs[1], inputs[2], param, config).node();
+            return Opr::make(
+                           inputs[0], inputs[1], inputs[2], param, execution_policy,
+                           config)
+                    .node();
         }
         return nullptr;
     }
@@ -223,8 +228,10 @@ struct PoolingLoadDumpImpl {
 
     static VarNode* make(
             const cg::VarNodeArray& inputs, const PoolingParam& param,
+            const megdnn::param::ExecutionPolicy& execution_policy,
             const OperatorNodeConfig& config) {
-        VarNode* ret = Maker0::template make<Opr>(inputs, param, config);
+        VarNode* ret =
+                Maker0::template make<Opr>(inputs, param, execution_policy, config);
         mgb_assert(ret);
         return ret;
     }
@@ -233,6 +240,29 @@ struct PoolingLoadDumpImpl {
             OprLoadContext& ctx, const cg::VarNodeArray& inputs,
             const OperatorNodeConfig& config) {
         auto param = ctx.read_param<PoolingParam>();
+        return make(inputs, param, {}, config)->owner_opr();
+    }
+};
+
+template <class Opr, class Maker0, typename GeneralOprParam = megdnn::param::ROIAlign>
+struct GeneralOprLoadDumpImpl {
+    static void dump(OprDumpContext& ctx, const cg::OperatorNodeBase& opr_) {
+        auto&& opr = opr_.cast_final_safe<Opr>();
+        ctx.write_param<GeneralOprParam>(opr.param());
+    }
+
+    static VarNode* make(
+            const cg::VarNodeArray& inputs, const GeneralOprParam& param,
+            const OperatorNodeConfig& config) {
+        VarNode* ret = Maker0::template make<Opr>(inputs, param, config);
+        mgb_assert(ret);
+        return ret;
+    }
+
+    static cg::OperatorNodeBase* load(
+            OprLoadContext& ctx, const cg::VarNodeArray& inputs,
+            const OperatorNodeConfig& config) {
+        auto param = ctx.read_param<GeneralOprParam>();
         return make(inputs, param, config)->owner_opr();
     }
 };
@@ -264,26 +294,26 @@ struct OprMaker<opr::LSQBackward, 5> {
 };
 template <>
 struct OprLoadDumpImpl<opr::AdaptivePoolingBackward, 0>
-        : public PoolingLoadDumpImpl<
+        : public GeneralOprLoadDumpImpl<
                   opr::AdaptivePoolingBackward,
                   MakeAdaptivePoolingBackwardCaller3<megdnn::AdaptivePoolingBackward>,
                   megdnn::param::AdaptivePooling> {};
 
 template <>
 struct OprLoadDumpImpl<opr::AdaptivePooling, 0>
-        : public PoolingLoadDumpImpl<
+        : public GeneralOprLoadDumpImpl<
                   opr::AdaptivePooling, MakeROIAlignCaller1<megdnn::AdaptivePooling>,
                   megdnn::param::AdaptivePooling> {};
 
 template <>
 struct OprLoadDumpImpl<opr::ROIAlign, 0>
-        : public PoolingLoadDumpImpl<
+        : public GeneralOprLoadDumpImpl<
                   opr::ROIAlign, MakeROIAlignCaller1<megdnn::ROIAlign>,
                   megdnn::param::ROIAlign> {};
 
 template <>
 struct OprLoadDumpImpl<opr::ROIAlignBackward, 0>
-        : public PoolingLoadDumpImpl<
+        : public GeneralOprLoadDumpImpl<
                   opr::ROIAlignBackward, MakeROIAlignCaller4<megdnn::ROIAlignBackward>,
                   megdnn::param::ROIAlign> {};
 
@@ -500,15 +530,29 @@ struct OprLoadDumpImpl<opr::DeformableConvBackwardFilter, 0>
                   opr::DeformableConvBackwardFilter,
                   MakeConvCaller5<megdnn::DeformableConvBackwardFilter>,
                   megdnn::Convolution> {};
+
+template <typename Opr>
+cg::OperatorNodeBase* opr_shallow_copy_conv(
+        const serialization::OprShallowCopyContext& ctx,
+        const cg::OperatorNodeBase& opr_, const VarNodeArray& inputs,
+        const OperatorNodeConfig& config) {
+    MGB_MARK_USED_VAR(ctx);
+    auto&& opr = opr_.cast_final_safe<Opr>();
+    return OprLoadDumpImpl<Opr, 0>::make(
+                   inputs, opr.param(), opr.execution_policy_transient(), config)
+            ->owner_opr();
+}
+
 }  // namespace serialization
 
 namespace opr {
 using ConvolutionV2 = Convolution;
 using ConvolutionBackwardDataV2 = ConvolutionBackwardData;
 using ConvolutionBackwardFilterV2 = ConvolutionBackwardFilter;
-MGB_SEREG_OPR(ConvolutionV2, 0);
-MGB_SEREG_OPR(ConvolutionBackwardDataV2, 0);
-MGB_SEREG_OPR(ConvolutionBackwardFilterV2, 0);
+MGB_SEREG_OPR_AND_REG_SHALLOW_COPY(ConvolutionV2, 0, opr_shallow_copy_conv);
+MGB_SEREG_OPR_AND_REG_SHALLOW_COPY(ConvolutionBackwardDataV2, 0, opr_shallow_copy_conv);
+MGB_SEREG_OPR_AND_REG_SHALLOW_COPY(
+        ConvolutionBackwardFilterV2, 0, opr_shallow_copy_conv);
 
 MGB_SEREG_OPR(Images2Neibs, 1);
 MGB_SEREG_OPR(Images2NeibsBackward, 2);
@@ -534,8 +578,8 @@ MGB_SEREG_OPR(LRN, 1);
 MGB_SEREG_OPR(LRNBackward, 3);
 using PoolingV1 = Pooling;
 using PoolingBackwardV1 = PoolingBackward;
-MGB_SEREG_OPR(PoolingV1, 1);
-MGB_SEREG_OPR(PoolingBackwardV1, 3);
+MGB_SEREG_OPR_AND_REG_SHALLOW_COPY(PoolingV1, 0, opr_shallow_copy_conv);
+MGB_SEREG_OPR_AND_REG_SHALLOW_COPY(PoolingBackwardV1, 0, opr_shallow_copy_conv);
 using AdaptivePoolingV1 = AdaptivePooling;
 using AdaptivePoolingBackwardV1 = AdaptivePoolingBackward;
 MGB_SEREG_OPR(AdaptivePoolingV1, 2);
@@ -548,12 +592,13 @@ using MaskConvolutionV2 = MaskConvolution;
 MGB_SEREG_OPR(MaskConvolutionV2, 3);
 MGB_SEREG_OPR(MaskPropagate, 1);
 
-MGB_SEREG_OPR(Convolution3D, 0);
-MGB_SEREG_OPR(Convolution3DBackwardData, 0);
-MGB_SEREG_OPR(Convolution3DBackwardFilter, 0);
+MGB_SEREG_OPR_AND_REG_SHALLOW_COPY(Convolution3D, 0, opr_shallow_copy_conv);
+MGB_SEREG_OPR_AND_REG_SHALLOW_COPY(Convolution3DBackwardData, 0, opr_shallow_copy_conv);
+MGB_SEREG_OPR_AND_REG_SHALLOW_COPY(
+        Convolution3DBackwardFilter, 0, opr_shallow_copy_conv);
 
 using ConvBiasForwardV4 = ConvBiasForward;
-MGB_SEREG_OPR(ConvBiasForwardV4, 0);
+MGB_SEREG_OPR_AND_REG_SHALLOW_COPY(ConvBiasForwardV4, 0, opr_shallow_copy_conv);
 
 using BatchNormV1 = BatchNorm;
 using BatchNormBackwardV1 = BatchNormBackward;
@@ -563,9 +608,10 @@ MGB_SEREG_OPR(BatchNormBackwardV1, 6);
 using LocalShareForwardV1 = LocalShareForward;
 using LocalShareBackwardDataV1 = LocalShareBackwardData;
 using LocalShareBackwardFilterV1 = LocalShareBackwardFilter;
-MGB_SEREG_OPR(LocalShareForwardV1, 0);
-MGB_SEREG_OPR(LocalShareBackwardDataV1, 0);
-MGB_SEREG_OPR(LocalShareBackwardFilterV1, 0);
+MGB_SEREG_OPR_AND_REG_SHALLOW_COPY(LocalShareForwardV1, 0, opr_shallow_copy_conv);
+MGB_SEREG_OPR_AND_REG_SHALLOW_COPY(LocalShareBackwardDataV1, 0, opr_shallow_copy_conv);
+MGB_SEREG_OPR_AND_REG_SHALLOW_COPY(
+        LocalShareBackwardFilterV1, 0, opr_shallow_copy_conv);
 
 using ROIAlignV1 = ROIAlign;
 using ROIAlignBackwardV1 = ROIAlignBackward;
@@ -574,9 +620,11 @@ MGB_SEREG_OPR(ROIAlignBackwardV1, 4);
 using DeformableConvForwardV1 = DeformableConvForward;
 using DeformableConvBackwardDataV1 = DeformableConvBackwardData;
 using DeformableConvBackwardFilterV1 = DeformableConvBackwardFilter;
-MGB_SEREG_OPR(DeformableConvForwardV1, 0);
-MGB_SEREG_OPR(DeformableConvBackwardDataV1, 0);
-MGB_SEREG_OPR(DeformableConvBackwardFilterV1, 0);
+MGB_SEREG_OPR_AND_REG_SHALLOW_COPY(DeformableConvForwardV1, 0, opr_shallow_copy_conv);
+MGB_SEREG_OPR_AND_REG_SHALLOW_COPY(
+        DeformableConvBackwardDataV1, 0, opr_shallow_copy_conv);
+MGB_SEREG_OPR_AND_REG_SHALLOW_COPY(
+        DeformableConvBackwardFilterV1, 0, opr_shallow_copy_conv);
 
 MGB_SEREG_OPR(CorrelationForward, 2);
 MGB_SEREG_OPR(CorrelationBackwardData1, 3);
@@ -586,7 +634,7 @@ MGB_SEREG_OPR(DeformablePSROIPoolingForward, 3);
 MGB_SEREG_OPR(DeformablePSROIPoolingBackward, 5);
 
 using BatchConvBiasForwardV1 = BatchConvBiasForward;
-MGB_SEREG_OPR(BatchConvBiasForwardV1, 0);
+MGB_SEREG_OPR_AND_REG_SHALLOW_COPY(BatchConvBiasForwardV1, 0, opr_shallow_copy_conv);
 MGB_SEREG_OPR(FakeQuant, 3);
 MGB_SEREG_OPR(FakeQuantBackward, 4);
 MGB_SEREG_OPR(TQT, 2);

src/opr/impl/dnn/pooling.cpp

@@ -32,8 +32,8 @@ PoolingForward::PoolingForward(
 }
 
 SymbolVar PoolingForward::make(
-        SymbolVar i0, const Param& param, const OperatorNodeConfig& config,
-        const ExecutionPolicy& policy) {
+        SymbolVar i0, const Param& param, const ExecutionPolicy& policy,
+        const OperatorNodeConfig& config) {
     intl::MegDNNOprInitInputsModifier<PoolingForward>::apply(param, {&i0});
     return i0.insert_single_output_opr<PoolingForward>(
             i0.node(), param, policy, config);
@@ -75,12 +75,13 @@ PoolingBackward::PoolingBackward(
                   0, true) {
     init_megdnn_opr(*this, param);
     add_input({i0, i1, i2});
+    m_policy = policy;
     intl::MegDNNOprInitPostCtor<PoolingBackward>::apply(*this);
 }
 
 SymbolVar PoolingBackward::make(
         SymbolVar i0, SymbolVar i1, SymbolVar i2, const Param& param,
-        const OperatorNodeConfig& config, const ExecutionPolicy& policy) {
+        const ExecutionPolicy& policy, const OperatorNodeConfig& config) {
     intl::MegDNNOprInitInputsModifier<PoolingBackward>::apply(param, {&i0, &i1, &i2});
     return i0.insert_single_output_opr<PoolingBackward>(
             i0.node(), i1.node(), i2.node(), param, policy, config);

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

@@ -26,8 +26,8 @@ MGE_WIN_DECLSPEC_FUC PoolingForward(
         VarNode* src, const Param& param, const ExecutionPolicy& policy,
         const OperatorNodeConfig& config);
 MGE_WIN_DECLSPEC_FUC static SymbolVar make(
-        SymbolVar src, const Param& param, const OperatorNodeConfig& config = {},
-        const ExecutionPolicy& policy = {});
+        SymbolVar src, const Param& param, const ExecutionPolicy& policy = {},
+        const OperatorNodeConfig& config = {});
 
 void init_output_static_infer_desc() override;
 
@@ -47,7 +47,7 @@ MGE_WIN_DECLSPEC_FUC PoolingBackward(
 
 MGE_WIN_DECLSPEC_FUC static SymbolVar make(
         SymbolVar src, SymbolVar dst, SymbolVar diff, const Param& param,
-        const OperatorNodeConfig& config = {}, const ExecutionPolicy& policy = {});
+        const ExecutionPolicy& policy = {}, const OperatorNodeConfig& config = {});
 
 MGE_WIN_DECLSPEC_FUC size_t get_workspace_size_bytes(
         const TensorShapeArray& input_shapes,

src/opr/test/algo_chooser.cpp

@@ -15,7 +15,9 @@
 #include "megbrain/opr/basic_arith.h"
 #include "megbrain/opr/blas.h"
 #include "megbrain/opr/dnn/convolution.h"
+#include "megbrain/opr/dnn/pooling.h"
 #include "megbrain/opr/tensor_manip.h"
+#include "megbrain/serialization/opr_shallow_copy.h"
 #include "megbrain/serialization/serializer.h"
 #include "megbrain/test/autocheck.h"
 #include "megbrain/test/helper.h"
@@ -32,39 +34,24 @@ using namespace mgb;
 
 namespace {
 
-#if MGB_CUDA
-#if MGB_ENABLE_FASTRUN
 template <typename MgbOpr, int arith>
 struct GraphMaker;
 
-template <typename MgbOpr>
-struct GraphMaker<MgbOpr, 2> {
-    SymbolVar operator()(
-            const std::array<cg::SymbolVar, 2>& inputs, typename MgbOpr::Param& param,
-            typename MgbOpr::ExecutionPolicy& policy) {
-        return MgbOpr::make(inputs[0], inputs[1], param, policy);
-    }
-};
-
 template <>
-struct GraphMaker<opr::ConvolutionBackwardData, 2> {
+struct GraphMaker<opr::Pooling, 1> {
     SymbolVar operator()(
-            const std::array<cg::SymbolVar, 2>& inputs,
-            opr::ConvolutionBackwardData::Param& param,
-            opr::ConvolutionBackwardData::ExecutionPolicy& policy) {
-        return opr::ConvolutionBackwardData::make_deconv(
-                inputs[0], inputs[1], param, policy);
+            const std::array<cg::SymbolVar, 1>& inputs, opr::Pooling::Param& param,
+            opr::Pooling::ExecutionPolicy& policy) {
+        return opr::Pooling::make(inputs[0], param, policy);
     }
 };
 
-template <>
-struct GraphMaker<opr::Convolution3DBackwardData, 2> {
+template <typename MgbOpr>
+struct GraphMaker<MgbOpr, 2> {
     SymbolVar operator()(
-            const std::array<cg::SymbolVar, 2>& inputs,
-            opr::Convolution3DBackwardData::Param& param,
-            opr::Convolution3DBackwardData::ExecutionPolicy& policy) {
-        return opr::Convolution3DBackwardData::make_deconv(
-                inputs[0], inputs[1], param, policy);
+            const std::array<cg::SymbolVar, 2>& inputs, typename MgbOpr::Param& param,
+            typename MgbOpr::ExecutionPolicy& policy) {
+        return MgbOpr::make(inputs[0], inputs[1], param, policy);
     }
 };
 
@@ -98,6 +85,37 @@ struct GraphMaker<MgbOpr, 5> {
     }
 };
 
+template <typename MgbOpr, int arith, typename dtype = dtype::Float32>
+void test_execution_policy_shallow_copy(
+        std::array<TensorShape, arith> shapes, typename MgbOpr::Param param = {}) {
+    using Policy = typename MgbOpr::ExecutionPolicy;
+
+    Policy policy;
+    policy.strategy = Policy::Strategy::PROFILE;
+
+    auto cn = CompNode::load("cpu0");
+    auto graph0 = ComputingGraph::make(), graph1 = ComputingGraph::make();
+    std::array<cg::SymbolVar, arith> inputs0;
+    VarNodeArray inputs1;
+    for (size_t i = 0; i < arith; ++i) {
+        HostTensorND hi{cn, shapes[i], dtype()};
+        inputs0[i] = opr::ImmutableTensor::make(*graph0, hi);
+        inputs1.push_back(opr::ImmutableTensor::make(*graph1, hi).node());
+    }
+
+    GraphMaker<MgbOpr, arith> graph_maker;
+    auto opr0 = graph_maker(inputs0, param, policy).node()->owner_opr();
+    auto opr1 = serialization::copy_opr_shallow(*opr0, inputs1, OperatorNodeConfig{});
+    auto m0 = &(opr0->template cast_final<MgbOpr>());
+    auto m1 = &(opr1->template cast_final<MgbOpr>());
+
+    ASSERT_EQ(policy.strategy, m0->execution_policy().strategy);
+    ASSERT_EQ(policy.strategy, m1->execution_policy().strategy);
+}
+
+#if MGB_CUDA
+#if MGB_ENABLE_FASTRUN
+
 template <typename MgbOpr, int arith, typename dtype = dtype::Float32>
 void test_fastrun_opr(
         std::array<TensorShape, arith> inps0, std::array<TensorShape, arith> inps1,
@@ -162,16 +180,24 @@ void test_fastrun_opr(
     size_t nr_set_total = expect_nr_cache_set_inp1 + nr_set_inp0;
     ASSERT_EQ(cache_set_history.size(), nr_set_total);
 }
+#endif  // MGB_ENABLE_FASTRUN
+#endif  // MGB_CUDA
 
+}  // anonymous namespace
+
+#if MGB_CUDA
+#if MGB_ENABLE_FASTRUN
 TEST(TestOprDNN, FastrunIgnoreBatchSizeConvolution) {
     REQUIRE_GPU(1);
     test_fastrun_opr<opr::Convolution, 2>(
             {TensorShape{12, 3, 36, 36}, TensorShape{4, 3, 3, 3}},
             {TensorShape{1, 3, 36, 36}, TensorShape{4, 3, 3, 3}});
 
-    test_fastrun_opr<opr::ConvolutionBackwardData, 2>(
-            {TensorShape{12, 4, 23, 29}, TensorShape{4, 5, 3, 2}},
-            {TensorShape{2, 4, 23, 29}, TensorShape{4, 5, 3, 2}});
+    test_fastrun_opr<opr::ConvolutionBackwardData, 3>(
+            {TensorShape{4, 5, 3, 2}, TensorShape{12, 4, 23, 29},
+             TensorShape{12, 5, 25, 30}},
+            {TensorShape{4, 5, 3, 2}, TensorShape{2, 4, 23, 29},
+             TensorShape{2, 5, 25, 30}});
 
     test_fastrun_opr<opr::ConvolutionBackwardFilter, 3>(
             {TensorShape{12, 4, 23, 29}, TensorShape{12, 5, 21, 28},
@@ -195,9 +221,11 @@ TEST(TestOprDNN, FastrunIgnoreBatchSizeConvolution3D) {
             {TensorShape{8, 4, 12, 13, 14}, TensorShape{4, 4, 3, 3, 3}},
             {TensorShape{3, 4, 12, 13, 14}, TensorShape{4, 4, 3, 3, 3}});
 
-    test_fastrun_opr<opr::Convolution3DBackwardData, 2>(
-            {TensorShape{14, 5, 12, 12, 16}, TensorShape{5, 5, 3, 3, 3}},
-            {TensorShape{4, 5, 12, 12, 16}, TensorShape{5, 5, 3, 3, 3}});
+    test_fastrun_opr<opr::Convolution3DBackwardData, 3>(
+            {TensorShape{5, 5, 3, 3, 3}, TensorShape{14, 5, 12, 12, 16},
+             TensorShape{14, 5, 14, 14, 18}},
+            {TensorShape{5, 5, 3, 3, 3}, TensorShape{4, 5, 12, 12, 16},
+             TensorShape{4, 5, 14, 14, 18}});
 
     test_fastrun_opr<opr::Convolution3DBackwardFilter, 3>(
             {TensorShape{64, 16, 18, 18, 18}, TensorShape{64, 16, 18, 18, 18},
@@ -295,6 +323,87 @@ TEST(TestOprDNN, FastrunIgnoreBatchSizeBatchedMatrixMul) {
 #endif  // MGB_ENABLE_FASTRUN
 #endif  // MGB_CUDA
 
-}  // anonymous namespace
+TEST(TestOprDNN, ExecutionPolicyShallowCopyConvolution) {
+    test_execution_policy_shallow_copy<opr::Convolution, 2>(
+            {TensorShape{12, 3, 36, 36}, TensorShape{4, 3, 3, 3}});
+
+    test_execution_policy_shallow_copy<opr::ConvolutionBackwardData, 3>(
+            {TensorShape{4, 5, 3, 2}, TensorShape{12, 4, 23, 29},
+             TensorShape{12, 5, 25, 30}});
+
+    test_execution_policy_shallow_copy<opr::ConvolutionBackwardFilter, 3>(
+            {TensorShape{12, 4, 23, 29}, TensorShape{12, 5, 21, 28},
+             TensorShape{5, 4, 3, 2}});
+}
+
+TEST(TestOprDNN, ExecutionPolicyShallowCopyConvBias) {
+    test_execution_policy_shallow_copy<opr::ConvBias, 3>(
+            {TensorShape{20, 16, 50, 50}, TensorShape{24, 16, 3, 3},
+             TensorShape{1, 24, 1, 1}});
+}
+
+TEST(TestOprDNN, ExecutionPolicyShallowCopyConvolution3D) {
+    test_execution_policy_shallow_copy<opr::Convolution3D, 2>(
+            {TensorShape{8, 4, 12, 13, 14}, TensorShape{4, 4, 3, 3, 3}});
+
+    test_execution_policy_shallow_copy<opr::Convolution3DBackwardData, 3>(
+            {TensorShape{5, 5, 3, 3, 3}, TensorShape{14, 5, 12, 12, 16},
+             TensorShape{14, 5, 14, 14, 18}});
+
+    test_execution_policy_shallow_copy<opr::Convolution3DBackwardFilter, 3>(
+            {TensorShape{64, 16, 18, 18, 18}, TensorShape{64, 16, 18, 18, 18},
+             TensorShape{16, 16, 1, 1, 1}});
+}
+
+TEST(TestOprDNN, ExecutionPolicyShallowCopyLocalShare) {
+    opr::LocalShare::Param local_share_param;
+    local_share_param.mode = opr::LocalShare::Param::Mode::CROSS_CORRELATION;
+    local_share_param.pad_h = local_share_param.pad_w = 1;
+    local_share_param.stride_h = local_share_param.stride_w = 1;
+    local_share_param.spatial_groups_h = local_share_param.spatial_groups_w = 2;
+    test_execution_policy_shallow_copy<opr::LocalShareForward, 2>(
+            {TensorShape{32, 2, 23, 23}, TensorShape{2, 2, 2, 2, 2, 7}},
+            local_share_param);
+
+    test_execution_policy_shallow_copy<opr::LocalShareBackwardData, 3>(
+            {TensorShape{3, 3, 128, 1, 1, 128}, TensorShape{32, 128, 24, 24},
+             TensorShape{32, 128, 24, 24}});
+
+    test_execution_policy_shallow_copy<opr::LocalShareBackwardFilter, 3>(
+            {TensorShape{12, 3, 36, 36}, TensorShape{12, 4, 35, 35},
+             TensorShape{3, 3, 3, 3, 3, 4}});
+}
+
+TEST(TestOprDNN, ExecutionPolicyShallowCopyDeformableConv) {
+    test_execution_policy_shallow_copy<opr::DeformableConvForward, 4>(
+            {TensorShape{12, 6, 20, 20}, TensorShape{6, 6, 3, 3},
+             TensorShape{12, 18, 18, 18}, TensorShape{12, 9, 18, 18}});
+
+    test_execution_policy_shallow_copy<opr::DeformableConvBackwardData, 5>(
+            {TensorShape{12, 6, 20, 20}, TensorShape{6, 6, 3, 3},
+             TensorShape{12, 18, 18, 18}, TensorShape{12, 9, 18, 18},
+             TensorShape{12, 6, 18, 18}});
+
+    test_execution_policy_shallow_copy<opr::DeformableConvBackwardFilter, 5>(
+            {TensorShape{12, 6, 20, 20}, TensorShape{6, 6, 3, 3},
+             TensorShape{12, 18, 18, 18}, TensorShape{12, 9, 18, 18},
+             TensorShape{12, 6, 18, 18}});
+}
+
+TEST(TestOprDNN, ExecutionPolicyShallowCopyMatrixMul) {
+    test_execution_policy_shallow_copy<opr::MatrixMul, 2>(
+            {TensorShape{10, 12}, TensorShape{12, 12}});
+
+    test_execution_policy_shallow_copy<opr::BatchedMatrixMul, 2>(
+            {TensorShape{12, 6, 8}, TensorShape{12, 8, 4}});
+}
+
+TEST(TestOprDNN, ExecutionPolicyShallowCopyPooling) {
+    test_execution_policy_shallow_copy<opr::Pooling, 1>({TensorShape{1, 20, 24, 24}});
+
+    test_execution_policy_shallow_copy<opr::PoolingBackward, 3>(
+            {TensorShape{1, 20, 24, 24}, TensorShape{1, 20, 12, 12},
+             TensorShape{1, 20, 12, 12}});
+}
 
 // vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}}

src/opr/test/dnn/pooling.cpp

@@ -135,7 +135,7 @@ TEST(TestOprDNN, PoolingExePolicy) {
     Policy policy;
     policy.strategy = strategy;
 
-    auto pooling = opr::PoolingForward::make(input, param, {}, policy);
+    auto pooling = opr::PoolingForward::make(input, param, policy);
 
     auto loss0 = opr::reduce_sum_sqr(pooling, pooling.make_scalar(1));
     auto grad = cg::grad(loss0, input, true, false);
@@ -187,7 +187,7 @@ TEST(TestOprDNN, PoolingForwardFastrun) {
     Policy policy;
     policy.strategy = strategy;
 
-    auto pooling = opr::PoolingForward::make(input, param, {}, policy);
+    auto pooling = opr::PoolingForward::make(input, param, policy);
 
     auto func = graph->compile({make_callback_copy(pooling, host_y)});
     func->execute().wait();

src/serialization/include/megbrain/serialization/sereg.h

@@ -253,4 +253,11 @@ struct IsComplete<T, decltype(void(sizeof(T)))> : std::true_type {};
             __caller_OprRegShallowCopy##_cls##_ins;                         \
     }
 
+/*!
+ * \brief register opr serialization and shallow copy methods
+ */
+#define MGB_SEREG_OPR_AND_REG_SHALLOW_COPY(_cls, _arity, _copy) \
+    MGB_SEREG_OPR(_cls, _arity)                                 \
+    MGB_REG_OPR_SHALLOW_COPY(_cls, ::mgb::serialization::_copy<_cls>)
+
 // vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}}