feat(src/core): free weight preprocessed weight

GitOrigin-RevId: 5f91acb909bdc58bfa8494c53665eaf7dfed15da

src/core/impl/graph/var_node.cpp

@@ -576,10 +576,10 @@ VarNode& VarNode::add_flag(Flag flag) {
 
 void VarNode::modify_flag(Flag delta, Flag new_flag) {
     if (contain_flag(Flag::FLAG_FREEZED)) {
-        mgb_assert((delta & (
-                    Flag::NO_MEM_RECLAIM |
-                    Flag::NO_SYS_STATIC_MEM_ALLOC |
-                    Flag::RT_FORCE_DYNAMIC_MEM_ALLOC)) == delta);
+        mgb_assert(
+                (delta & (Flag::NO_MEM_RECLAIM | Flag::NO_SYS_STATIC_MEM_ALLOC |
+                          Flag::RT_FORCE_DYNAMIC_MEM_ALLOC)) == delta ||
+                (new_flag & Flag::MEMORY_NO_NEED));
 
         mgb_assert(!ComputingGraphImpl::downcast(owner_graph())->
                 var_node_mem_manager().optimize_started(),

src/core/impl/graph/var_node_mem_mgr.cpp

@@ -24,6 +24,8 @@
 #include "megbrain/utils/timer.h"
 #include "megbrain/utils/arith_helper.h"
 
+#include "megbrain/opr/io.h"
+
 #include <chrono>
 
 using namespace mgb;
@@ -36,7 +38,6 @@ void call_mem_status_changed(cg::OperatorNodeBase* opr) {
     if (cb.on_mem_status_changed.valid())
         cb.on_mem_status_changed.val()();
 }
-
 }  // namespace
 
 /* ==================== StaticDeviceMemoryManager ==================== */
@@ -393,11 +394,12 @@ bool VarNodeMemManager::alloc_var_node_mem_static() {
 
 bool VarNodeMemManager::update_static_alloc_plan() {
     // check whether unchanged
+    bool free_no_need_memory = free_combine_memory_no_need_var();
     if (!m_owner_graph->static_infer_comp_seq_manager()
                  .update_static_check_shape_change() &&
         !m_first_static_plan_run &&
         !m_impure_mem_plan_mgr.check_need_realloc()) {
-        return false;
+        return false || free_no_need_memory;
     }
 
     if (m_first_static_plan_run)
@@ -494,6 +496,96 @@ bool VarNodeMemManager::make_static_var_tensor_from_alloc_plan() {
     return true;
 }
 
+bool VarNodeMemManager::free_combine_memory_no_need_var() {
+    if (!m_owner_graph->options().graph_opt.weight_preprocess ||
+        m_already_free_no_need_mem) {
+        return false;
+    }
+    bool reordered = false;
+    //! free no need storage
+    for (auto opr : *m_opr_seq) {
+        if (opr->try_cast_final<opr::SharedDeviceTensor>() ||
+            opr->try_cast_final<opr::SharedDeviceTensorWithFormat>()) {
+            auto opr_base = static_cast<opr::intl::SharedDeviceTensorBase*>(opr);
+            auto var = opr_base->output(0);
+            if (var->contain_flag(VarNode::Flag::MEMORY_NO_NEED) &&
+                var->dev_tensor_valid() && !var->dev_tensor().empty()) {
+                //! Only the tensor share count is 1, it can be free
+                if (opr_base->dev_data().use_count() == 1) {
+                    auto layout = var->layout();
+                    var->m_dev_tensor.reset(
+                            DeviceTensorStorage{var->comp_node()}, layout);
+                    opr_base->free_dev_data();
+                    mgb_log_debug(
+                            "preprocessed weight is freed, var name = %s, "
+                            "var layout = %s",
+                            var->name().c_str(), layout.to_string().c_str());
+                }
+                m_already_free_no_need_mem = true;
+            }
+        }
+        bool memory_need_reorder = false;
+        if (opr->try_cast_final<opr::MultipleDeviceTensorHolder>() ||
+            opr->try_cast_final<opr::MultipleDeviceTensorWithFormatHolder>()) {
+            auto opr_base =
+                    static_cast<opr::intl::MultipleDeviceTensorHolderBase*>(
+                            opr);
+            for (size_t index = 0; index < opr_base->output().size(); index++) {
+                auto var = opr_base->output(index);
+                if (var->contain_flag(VarNode::Flag::MEMORY_NO_NEED) &&
+                    var->dev_tensor_valid() && !var->dev_tensor().empty()) {
+                    //! Only the tensor share count is 1, it can be free
+                    if (opr_base->values()[index].use_count() == 1) {
+                        auto layout = var->layout();
+                        var->m_dev_tensor.reset(
+                                DeviceTensorStorage{var->comp_node()}, layout);
+                        opr_base->mutable_values()[index]->reset(
+                                DeviceTensorStorage{var->comp_node()}, layout);
+                        memory_need_reorder = true;
+                        mgb_log_debug(
+                                "preprocessed weight is freed, var name "
+                                "= %s, var layout = %s",
+                                var->name().c_str(),
+                                layout.to_string().c_str());
+                    }
+                    m_already_free_no_need_mem = true;
+                }
+            }
+        }
+        //! recorder the other needed outputs, because they share the
+        //! same chunk of mem in device with no needed var, see
+        //! BatchedDeviceValueLoader
+        if (memory_need_reorder) {
+            auto opr_base =
+                    static_cast<opr::intl::MultipleDeviceTensorHolderBase*>(
+                            opr);
+            auto comp_node = opr_base->output(0)->comp_node();
+            bool is_device_opr =
+                    comp_node.mem_node() != CompNode::default_cpu().mem_node();
+            if (memory_need_reorder && is_device_opr) {
+                for (size_t index = 0; index < opr_base->output().size();
+                     index++) {
+                    auto var = opr_base->output(index);
+                    if (!var->contain_flag(VarNode::Flag::MEMORY_NO_NEED)) {
+                        DeviceTensorStorage storage(var->comp_node());
+                        size_t size = var->layout().span().dist_byte();
+                        storage.ensure_size(size);
+                        storage.copy_from(var->m_dev_tensor.storage(), size);
+
+                        var->m_dev_tensor.reset(storage, var->layout());
+                        opr_base->mutable_values()[index]->reset(storage,
+                                                                var->layout());
+                        reordered = true;
+                    }
+                }
+                //! sync to make sure memcopy is finished
+                comp_node.sync();
+            }
+        }
+    }
+    return reordered;
+}
+
 void VarNodeMemManager::init_dynamic_alloc_opr_info() {
     mgb_assert(m_first_static_plan_run);
     m_need_post_exec_action_vars.clear();

src/core/impl/graph/var_node_mem_mgr.h

@@ -173,6 +173,14 @@ class VarNodeMemManager {
          */
         bool alloc_var_node_mem_static();
 
+        /*!
+         * \brief free the memory of var with MEMORY_NO_NEED flag
+         *
+         * \return whether memory of MEMORY_NO_NEED var or related other var
+         * memory changed
+         */
+        bool free_combine_memory_no_need_var();
+
         /*!
          * \brief initialize static memory allocation plan
          *
@@ -407,7 +415,8 @@ class VarNodeMemManager {
             bool check_need_realloc();
         };
 
-        bool m_first_static_plan_run = true, m_optimize_started = false;
+        bool m_first_static_plan_run = true, m_optimize_started = false,
+             m_already_free_no_need_mem = false;
         ComputingGraphImpl *m_owner_graph;
         ThinHashMap<VarNode*, VarNodeMemTrait> m_node_mem_trait;
         NullableHashMap<OperatorNodeBase*, DynamicAllocOprInfo>

src/core/impl/tensor.cpp

@@ -449,7 +449,11 @@ DEF(resize, &)(const TensorShape& shape) {
 }
 
 DEF(reset, &)(TensorStorage storage, const TensorLayout &layout) {
-    mgb_assert(!layout.ndim || storage.valid_span(layout.span()));
+    //! The storage to be reset is either satisfy the layout or empty.
+    //! Empty storage is used after weight preprocess for saving memory and
+    //! checking layout when running
+    mgb_assert(!layout.ndim || storage.valid_span(layout.span()) ||
+               storage.empty());
     m_storage = std::move(storage);
     m_layout = layout;
     return static_cast<ChainReturnType&>(*this);

src/core/include/megbrain/graph/cg.h

@@ -98,7 +98,8 @@ struct GraphCommonOptimizeOptions {
     //! whether to enable fast-run profiled winograd opr replace
     bool weight_winograd_transform = false;
     //! whether to enable weight preprocess, if enabled it may use more
-    //! memory, default disable now
+    //! memory, default disable now, when weight preprocess is enabled, the
+    //! input shape should no change
     bool weight_preprocess = false;
     enum LayoutTransform : uint32_t {
         DEFAULT,

src/core/include/megbrain/graph/var_node.h

@@ -589,7 +589,7 @@ class VarNode final: public GraphNodeBase {
         friend class imperative::ProxyGraph;
 };
 
-enum class VarNode::Flag: uint32_t {
+enum class VarNode::Flag : uint32_t {
     //! do not allocate memory by the system allocator even if shape could be
     //! inferred
     NO_SYS_MEM_ALLOC = 1 << 0,
@@ -667,6 +667,12 @@ enum class VarNode::Flag: uint32_t {
      * after FLAG_FREEZED is present.
      */
     FLAG_FREEZED = 1 << 10,
+
+    /*!
+     * this flag indicates that data of this var has been processed and no need
+     * later, it can be freed, this is used in weight preprocess for memory save
+     */
+    MEMORY_NO_NEED = 1 << 11,
 };
 
 MGB_DEF_ENUM_CLASS_BIT_OPR(VarNode::Flag)

src/core/test/graph/misc.cpp

@@ -1920,4 +1920,236 @@ TEST(TestGraph, NaiveRecord2NCHW44) {
     func->execute().wait();
 }
 
+namespace {
+template <typename DnnOp, typename... Args>
+typename DnnOp::Algorithm* try_find_any_weight_preprocess_algo(
+        DnnOp* dnn_op, const char* mgb_info, Maybe<bool>& found,
+        Args&& ...args) {
+    if (found.valid()) {
+        if (found.val()) {
+            return dnn_op->execution_policy().algorithm;
+        } else {
+            return nullptr;
+        }
+    }
+    for (auto&& algo : dnn_op->get_all_algorithms(
+            std::forward<Args>(args)...)) {
+        dnn_op->execution_policy().algorithm = algo;
+        auto layouts = dnn_op->deduce_preprocessed_filter_layout(
+                std::forward<Args>(args)...);
+        if (layouts.empty()) continue;
+        bool valid = false;
+        for (auto&& l: layouts) {
+            if (!l.is_empty()) {
+                valid = true;
+                break;
+            }
+        }
+        if (valid) {
+            found.emplace(true);
+            return algo;
+        }
+    }
+    found.emplace(false);
+    mgb_log_warn("Can't find weight preprocess algo for op %s", mgb_info);
+    return nullptr;
+}
+
+void test_free_memory_in_weight_preprocess(int record_level, CompNode cn) {
+    HostTensorGenerator<> gen;
+    auto graph = ComputingGraph::make();
+    graph->options().graph_opt.weight_preprocess = true;
+    graph->options().comp_node_seq_record_level = record_level;
+    auto mkvar = [&](const char* name, const TensorShape& shp) {
+        return opr::Host2DeviceCopy::make(*graph, gen(shp, cn)).rename(name);
+    };
+    auto mkcvar = [&](const char* name, const TensorShape& shp) {
+        return opr::SharedDeviceTensor::make_const(*graph, *gen(shp, cn))
+                .rename(name);
+    };
+    auto x = mkvar("x", {1, 32, 16, 16});
+    // ConvBias test dense
+    opr::ConvBias::Param param_conv_bias;
+    param_conv_bias.pad_h = param_conv_bias.pad_w = 0;
+    param_conv_bias.sparse = opr::ConvBias::Param::Sparse::DENSE;
+    auto w1 = mkcvar("w1", {32, 32, 1, 1}), b1 = mkcvar("b1", {1, 32, 1, 1});
+    auto conv1 = opr::ConvBias::make(x, w1, b1, param_conv_bias);
+    Maybe<bool> wp1, wp2;
+    conv1.node()->owner_opr()->cast_final_safe<opr::ConvBias>()
+        .setup_algo_chooser([&](const cg::OperatorNodeBase* opr) {
+            return try_find_any_weight_preprocess_algo(
+                opr->cast_final_safe<opr::ConvBias>().megdnn_opr(),
+                opr->cname(), wp1,
+                opr->input(0)->layout(), opr->input(1)->layout(),
+                opr->input(2)->layout(), TensorLayout{},
+                opr->output(0)->layout());
+    });
+    // Convolution
+    opr::Convolution::Param param_conv;
+    param_conv.pad_h = param_conv.pad_w = 0;
+    param_conv.sparse = opr::Convolution::Param::Sparse::DENSE;
+    auto w2 = mkcvar("w2", {32, 32, 1, 1});
+    auto y = opr::Convolution::make(conv1, w2, param_conv);
+    y.node()->owner_opr()->cast_final_safe<opr::Convolution>()
+        .setup_algo_chooser([&](const cg::OperatorNodeBase* opr) {
+            return try_find_any_weight_preprocess_algo(
+                opr->cast_final_safe<opr::Convolution>().megdnn_opr(),
+                opr->cname(), wp2,
+                opr->input(0)->layout(), opr->input(1)->layout(),
+                opr->output(0)->layout());
+    });
+
+    HostTensorND host_y;
+    auto func =graph->compile({make_callback_copy(y, host_y)});
+    //!flag the no need memory of var
+    func->execute();
+    //!free the no need memory of var
+    func->execute();
+    auto check = [&](SymbolVar v) {
+        ASSERT_TRUE(v.node()->contain_flag(VarNode::Flag::MEMORY_NO_NEED));
+        ASSERT_TRUE(v.node()->dev_tensor().empty());
+        ASSERT_TRUE(v.node()->owner_opr()
+                ->cast_final_safe<opr::SharedDeviceTensor>()
+                .get_dev_tensor()
+                .empty());
+    };
+    ASSERT_TRUE(wp1.valid() && wp2.valid());
+    if (wp1.val()) {
+        check(w1);
+    }
+    if (wp2.val()) {
+        check(w2);
+    }
+}
+} // anonymous namespace
+
+TEST(TestGraph, FreeMemoryInWeightPreprocess) {
+    test_free_memory_in_weight_preprocess(0, CompNode::load("xpu0"));
+}
+
+TEST(TestGraph, RecordFreeMemoryInWeightPreprocess) {
+    test_free_memory_in_weight_preprocess(1, CompNode::load("cpu0"));
+}
+
+namespace {
+MGB_DEFINE_OPR_CLASS(HostValueReader, cg::SingleCNOutshapePureByInshapeOprBase) // {
+    void scn_do_execute() override {
+        auto&& hv = owner_graph()->static_infer_manager().infer_value(input(0));
+        MGB_MARK_USED_VAR(hv);
+    }
+
+    NodeProp* do_make_node_prop() const override {
+        auto ret = Super::do_make_node_prop();
+        ret->dep_map()[input(0)] = NodeProp::DepType::HOST_VALUE;
+        return ret;
+    }
+
+    void get_output_var_shape(
+            const TensorShapeArray &,
+            TensorShapeArray &out_shape) const override {
+        out_shape.at(0) = {};
+    }
+
+    public:
+        HostValueReader(VarNode* inp)
+            : Super{inp->owner_graph(), {}, "host_value_reader", {inp}} {
+            add_input({inp});
+            using F = VarNode::Flag;
+            add_output(None)
+                    ->add_flag(F::ALLOW_EMPTY_SHAPE)
+                    .add_flag(F::VOLATILE_CONTENT);
+        }
+
+        static SymbolVar make(SymbolVar inp) {
+            return inp.node()->owner_graph()->insert_opr(
+                std::make_unique<HostValueReader>(inp.node()))->output(0);
+        }
+};
+MGB_DYN_TYPE_OBJ_FINAL_IMPL(HostValueReader);
+}
+
+TEST(TestGraph, FreeMemoryInWeightPreprocessWithValueInfer) {
+    HostTensorGenerator<> gen;
+    CompNode cn = CompNode::load("xpux");
+    auto graph = ComputingGraph::make();
+    graph->options().graph_opt.weight_preprocess = true;
+    graph->options().var_sanity_check_first_run = false;
+    auto mkvar = [&](const char* name, const TensorShape& shp) {
+        return opr::Host2DeviceCopy::make(*graph, gen(shp, cn)).rename(name);
+    };
+    auto mkcvar = [&](const char* name, const TensorShape& shp) {
+        return opr::SharedDeviceTensor::make_const(*graph, *gen(shp, cn))
+                .rename(name);
+    };
+    auto x = mkvar("x", {1, 32, 16, 16});
+    auto w = mkcvar("w", {32, 32, 1, 1});
+    auto y = opr::Convolution::make(x, w);
+    Maybe<bool> found;
+    y.node()->owner_opr()->cast_final_safe<opr::Convolution>()
+        .setup_algo_chooser([&](const cg::OperatorNodeBase* opr) {
+            return try_find_any_weight_preprocess_algo(
+                opr->cast_final_safe<opr::Convolution>().megdnn_opr(),
+                opr->cname(), found,
+                opr->input(0)->layout(), opr->input(1)->layout(),
+                opr->output(0)->layout());
+    });
+    auto reader = HostValueReader::make(w);
+
+    HostTensorND host_y;
+    auto func = graph->compile({make_callback_copy(y, host_y), {reader, {}}});
+    func->execute();
+    // FIXME: failed on second execution due to requiring host value of the empty
+    // tensor which was freed in weight preprocess
+    func->execute();
+    ASSERT_FALSE(w.node()->contain_flag(VarNode::Flag::MEMORY_NO_NEED));
+    ASSERT_FALSE(w.node()->dev_tensor().empty());
+    ASSERT_FALSE(w.node()->owner_opr()
+                         ->cast_final_safe<opr::SharedDeviceTensor>()
+                         .get_dev_tensor()
+                         .empty());
+}
+
+TEST(TestGraph, FreeMemoryInWeightPreprocessWithMultiReader) {
+    HostTensorGenerator<> gen;
+    CompNode cn = CompNode::load("xpux");
+    auto graph = ComputingGraph::make();
+    graph->options().graph_opt.weight_preprocess = true;
+    graph->options().var_sanity_check_first_run = false;
+    graph->options().graph_opt_level = 0;
+    auto mkvar = [&](const char* name, const TensorShape& shp) {
+        return opr::Host2DeviceCopy::make(*graph, gen(shp, cn)).rename(name);
+    };
+    auto mkcvar = [&](const char* name, const TensorShape& shp) {
+        return opr::SharedDeviceTensor::make_const(*graph, *gen(shp, cn))
+                .rename(name);
+    };
+    auto x = mkvar("x", {1, 32, 16, 16});
+    auto w = mkcvar("w", {32, 32, 1, 1});
+    auto y = opr::Convolution::make(x, w);
+    Maybe<bool> found;
+    y.node()->owner_opr()->cast_final_safe<opr::Convolution>()
+        .setup_algo_chooser([&](const cg::OperatorNodeBase* opr) {
+            return try_find_any_weight_preprocess_algo(
+                opr->cast_final_safe<opr::Convolution>().megdnn_opr(),
+                opr->cname(), found,
+                opr->input(0)->layout(), opr->input(1)->layout(),
+                opr->output(0)->layout());
+    });
+    auto y1 = w * 2 + 1;
+
+    HostTensorND host_y, host_y1;
+    auto func = graph->compile({
+        make_callback_copy(y, host_y), make_callback_copy(y1, host_y1)});
+    func->execute();
+    // FIXME: failed on second execution due to calculate expression
+    // (w * 2 + 1) with empty tensor
+    func->execute();
+    ASSERT_FALSE(w.node()->contain_flag(VarNode::Flag::MEMORY_NO_NEED));
+    ASSERT_FALSE(w.node()->dev_tensor().empty());
+    ASSERT_FALSE(w.node()->owner_opr()
+                        ->cast_final_safe<opr::SharedDeviceTensor>()
+                        .get_dev_tensor()
+                        .empty());
+}
+
 // vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}}

src/opr/impl/dnn/convolution.cpp

@@ -138,39 +138,36 @@ public:
 
 void mixin::WeightPreprocessExecutor::mixin_update_preprocessed_filter(
         cg::OperatorNodeBase& opr) {
-    if (!mixin_allow_weight_preprocess(opr))
+    if (!mixin_allow_weight_preprocess(opr)) {
         return;
-
+    }
     auto new_layout = deduce_preprocessed_filter_layout();
+    size_t new_size = new_layout.size();
+    //! No preprocess layout means no need weight preprocess
     if (new_layout.empty()) {
-        // Weight preprocess was needed before, but no longer needed.
-        if (m_preprocessed_filter) {
-            m_preprocessed_filter.reset();
-            m_filter_storage.clear();
+        return;
+    }
+    //! all layouts arm empty means no need weight preprocess
+    bool layout_valid = false;
+    for (auto&& layout : new_layout) {
+        if (!layout.is_empty()) {
+            layout_valid = true;
         }
+    }
+    if (!layout_valid) {
         return;
     }
 
-    bool should_update = false;
-    size_t new_size = new_layout.size();
-    if (!m_preprocessed_filter ||
-        m_preprocessed_filter->tensors.size() != new_size) {
-        should_update = true;
-    } else {
+    if (m_preprocessed_filter) {
         for (size_t i = 0; i < new_size; i++) {
-            if (!new_layout[i].eq_layout(
-                        m_preprocessed_filter->tensors[i].layout)) {
-                should_update = true;
-                break;
-            }
+            mgb_assert(new_layout[i].eq_layout(
+                               m_preprocessed_filter->tensors[i].layout),
+                       "weight preprocess layout changed, please keep input "
+                       "shape unchanged when weight preprocess is enabled");
         }
-    }
-    if (!should_update)
         return;
-
-    if (!m_preprocessed_filter) {
-        m_preprocessed_filter.reset(new PreprocessedFilter{});
     }
+    m_preprocessed_filter.reset(new PreprocessedFilter{});
     m_preprocessed_filter->tensors.resize(new_size);
     m_filter_storage.resize(new_size);
     m_preprocessed_filter->algorithm_id = nullptr;
@@ -327,6 +324,14 @@ void ConvolutionForward::scn_do_execute_preprocess() {
             input(0)->layout(), input(1)->dev_tensor().as_megdnn(),
             output(0)->layout(), preprocessed_filter(),
             intl::get_megdnn_workspace_from_var(output().back()));
+    //! Flag the input(1) no use later, which can be freed when no other
+    //! var depend on its dev_value, host_value and shape.
+    auto receiver_info =
+            input(1)->owner_graph()->var_receiver_in_current_comp_seq(input(1));
+    if (receiver_info.dev_value == 1 && receiver_info.host_value == 0 &&
+        receiver_info.shape == 0) {
+        input(1)->add_flag(VarNode::Flag::MEMORY_NO_NEED);
+    }
 }
 
 /* ==================== ConvolutionBackwardData  ==================== */
@@ -959,6 +964,14 @@ void ConvBiasForward::scn_do_execute_preprocess() {
             input(0)->layout(), input(1)->dev_tensor().as_megdnn(), bias_layout,
             z_layout, output(0)->layout(), preprocessed_filter(),
             intl::get_megdnn_workspace_from_var(output().back()));
+    //! Flag the input(1) no use later, which can be freed when no other
+    //! var depend on its dev_value, host_value and shape.
+    auto receiver_info =
+            input(1)->owner_graph()->var_receiver_in_current_comp_seq(input(1));
+    if (receiver_info.dev_value == 1 && receiver_info.host_value == 0 &&
+        receiver_info.shape == 0) {
+        input(1)->add_flag(VarNode::Flag::MEMORY_NO_NEED);
+    }
 }
 
 /* ===================== LocalShareForward ==================== */

src/opr/impl/io.cpp

@@ -142,8 +142,10 @@ void intl::DeviceTensorHolder::add_output(DType dtype) {
 }
 
 void intl::DeviceTensorHolder::record_execute_deps(ExecDependencyArray& deps) {
-    deps.emplace_back(
-            std::make_unique<DevValueExecDep>(get_dev_tensor().storage()));
+    if (!output(0)->contain_flag(VarNode::Flag::MEMORY_NO_NEED)) {
+        deps.emplace_back(
+                std::make_unique<DevValueExecDep>(get_dev_tensor().storage()));
+    }
 }
 
 /* ===================== Host2DeviceCopy ===================== */
@@ -801,14 +803,19 @@ class intl::MultipleDeviceTensorHolderBase::DevValuesExecDep final
     SmallVector<DeviceTensorStorage> m_vals;
 
 public:
-    explicit DevValuesExecDep(const ValueArray& vals) {
-        for (auto&& val : vals) {
-            m_vals.emplace_back(std::move(val->storage()));
+    explicit DevValuesExecDep(const ValueArray& vals,
+                              MultipleDeviceTensorHolderBase* opr) {
+        mgb_assert(vals.size() == opr->output().size(),
+                   "the output value size is diff from output var size");
+        for (size_t index = 0; index < vals.size(); index++) {
+            if (!opr->output(index)->contain_flag(
+                        VarNode::Flag::MEMORY_NO_NEED)) {
+                m_vals.emplace_back(std::move(vals[index]->storage()));
+            }
         }
     }
 };
 
-
 intl::MultipleDeviceTensorHolderBase::MultipleDeviceTensorHolderBase(
         ComputingGraph& graph, ValueArray values,
         const OperatorNodeConfig& config)
@@ -887,8 +894,7 @@ intl::MultipleDeviceTensorHolderBase::do_make_node_prop() const {
 
 void intl::MultipleDeviceTensorHolderBase::record_execute_deps(
         ExecDependencyArray& deps) {
-    deps.emplace_back(
-            std::make_unique<DevValuesExecDep>(values()));
+    deps.emplace_back(std::make_unique<DevValuesExecDep>(values(), this));
 }
 
 /* ===================== MultipleDeviceTensorHolder ===================== */

src/opr/impl/search_policy/algo_chooser.cpp

@@ -173,9 +173,15 @@ size_t AlgoChooser<Opr>::setup_algo(const ConvTensorLayouts& layouts,
         return 0;
     }
 
+    ImplAlgo algo = nullptr;
     ExeContext ctx(layouts, megdnn_opr, mgb_opr, allow_weight_preprocess);
 
-    auto algo = get_algo(ctx);
+    if (auto algo_choose_hook = mgb_opr->algo_chooser()) {
+        algo = algo_choose_hook(mgb_opr);
+    }
+    if (!algo) {
+        algo = get_algo(ctx);
+    }
     size_t workspace = ctx.get_workspace_size_bytes(algo);
     mgb_log_debug(
             "%s: tensor layouts(%s %s, %s %s) -> (%s %s): algo=%s "
@@ -360,16 +366,29 @@ AlgoChooser<Opr>::ExeContext::construct_fake_preprocess_filter() const {
         if (!m_allow_weight_preprocess)
             return;
         auto opr = _(m_megdnn_opr);
-        auto layout = APPLY(opr->deduce_preprocessed_filter_layout(args...),
-                            m_layouts);
-        if (layout.empty())
+        auto layouts = APPLY(opr->deduce_preprocessed_filter_layout(args...),
+                             m_layouts);
+        //! No preprocess layout means no need weight preprocess
+        if (layouts.empty()) {
             return;
+        }
+        //! all layouts arm empty means no need weight preprocess
+        bool layout_valid = false;
+        for (auto&& layout : layouts) {
+            if (!layout.is_empty()) {
+                layout_valid = true;
+            }
+        }
+        if (!layout_valid) {
+            return;
+        }
+
         result = PreprocessFilter<Opr>{};
         auto& res = result.val();
         res.algorithm_id = nullptr;
-        res.tensors.resize(layout.size());
-        for (size_t i = 0; i < layout.size(); i++) {
-            res.tensors[i] = megdnn::TensorND(nullptr, layout[i]);
+        res.tensors.resize(layouts.size());
+        for (size_t i = 0; i < layouts.size(); i++) {
+            res.tensors[i] = megdnn::TensorND(nullptr, layouts[i]);
         }
     });
     return result;

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

@@ -25,6 +25,9 @@ namespace mixin {
 class Convolution {
     public:
         using ExecutionPolicy = megdnn::param::ExecutionPolicy;
+        using Algorithm = megdnn::detail::Algorithm;
+        using AlgoChooserHook =
+                std::function<Algorithm*(const OperatorNodeBase*)>;
 
         const ExecutionPolicy& execution_policy() const {
             if (!m_policy_accessed) {
@@ -55,6 +58,16 @@ class Convolution {
 
         virtual std::pair<const void*, size_t> param_blob() const = 0;
 
+        /*!
+         * \brief register a hook to implement custom algo chooser
+         */
+        void setup_algo_chooser(AlgoChooserHook&& func) {
+            m_algo_chooser = func;
+        }
+        AlgoChooserHook algo_chooser() const {
+            return m_algo_chooser;
+        }
+
     protected:
         ~Convolution();
 
@@ -63,6 +76,8 @@ class Convolution {
 
         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

src/opr/include/megbrain/opr/io.h

@@ -99,6 +99,11 @@ MGB_DEFINE_CLS_WITH_SUPER(SharedDeviceTensorBase, DeviceTensorHolder) // {
             return *m_dev_data;
         }
 
+        void free_dev_data() {
+            m_dev_data->reset(DeviceTensorStorage{m_dev_data->comp_node()},
+                              m_dev_data->layout());
+        }
+
         const std::shared_ptr<DeviceTensorND>& dev_data() const {
             return m_dev_data;
         }
@@ -122,6 +127,10 @@ public:
                                    const OperatorNodeConfig& config);
     const ValueArray& values() const { return m_values; }
 
+    ValueArray& mutable_values() {
+        return m_values;
+    }
+
 protected:
     ValueArray m_values;
 
@@ -292,7 +301,7 @@ MGB_DEFINE_OPR_CLASS(SharedDeviceTensor, intl::SharedDeviceTensorBase) // {
         static SymbolVar make_const(ComputingGraph& graph,
                                     const HostTensorND& value,
                                     const OperatorNodeConfig& config = {}) {
-            return make(graph, value, false, config);
+            return make(graph, value, true, config);
         }
 };