feat(mgb): add PackAllReducePass

GitOrigin-RevId: 59c1b45393

python_module/megengine/_internal/comp_graph_tools.py

@@ -260,3 +260,15 @@ def replace_oprs(dst, oprmap):
         repl_dst_vec.push_back(j)
 
     return _mgb._replace_oprs(repl_src_vec, repl_dst_vec, dst_vec)
+
+
+def set_priority_to_id(dest_vars):
+    """For all oprs in the subgraph constructed by dest_vars
+       set its priority to id if its original priority is zero
+    :param dest_vars: target vars representing the graph
+    """
+    dest_vec = _mgb._VectorSymbolVar()
+    for i in dest_vars:
+        assert isinstance(i, _mgb.SymbolVar)
+        dest_vec.push_back(i)
+    _mgb._set_priority_to_id(dest_vec)

python_module/megengine/jit/__init__.py

@@ -84,6 +84,8 @@ class trace:
     :param log_level: Log level.
     :param sublinear_memory_config: Configuration for sublinear memory optimization.
         If not None, it enables sublinear memory optimization with given setting.
+    :param allreduce_pack_max_size: Maximum size of an allreduce pack in MB.
+        If not None, multiple gradients will be packed and synchronized together
     :param profiling: Whether to profile compiled trace. Default: False
     """
 
@@ -107,6 +109,7 @@ class trace:
         opt_level: int = None,
         log_level: int = None,
         sublinear_memory_config: SublinearMemoryConfig = None,
+        allreduce_pack_max_size: int = None,
         profiling: bool = False
     ):
         self.__wrapped__ = func
@@ -114,6 +117,7 @@ class trace:
         self._graph_opt_level = opt_level
         self._log_level = log_level
         self._sublinear_memory_config = sublinear_memory_config
+        self._allreduce_pack_max_size = allreduce_pack_max_size
         self._status = self._UNSTARTED
         self._args = None
         self._kwargs = None
@@ -313,6 +317,9 @@ class trace:
                 "sublinear_mem_cofig.num_worker",
                 self._sublinear_memory_config.num_worker,
             )
+        # pack allreduce
+        if self._allreduce_pack_max_size is not None:
+            cg.set_option("allreduce_pack_max_size", self._allreduce_pack_max_size)
         # profile
         if self._profiling:
             self._profiler = CompGraphProfiler(cg)
@@ -391,6 +398,7 @@ class trace:
                     outputs = [outputs]
                 # _run_wrapped has checked validity of outputs
                 self._sym_outputs = tuple(i._symvar for i in outputs)
+            mgb.comp_graph_tools.set_priority_to_id(self._outspec)
             self._compiled_func = graph.get_default_graph().compile(None, self._outspec)
 
     def trace(self, *args: Tensor, **kwargs):

python_module/megengine/optimizer/optimizer.py

@@ -159,7 +159,6 @@ class Optimizer(metaclass=ABCMeta):
         :param loss: The obtained loss tensor
         """
         rst = []
-        priority = 0
         params = []
         for group in self.param_groups:
             for param in group["params"]:
@@ -180,14 +179,14 @@ class Optimizer(metaclass=ABCMeta):
 
         for param, grad in zip(params, grads):
             if is_distributed():
-                priority += 1
-                with opr_priority_scope(cg, -priority):
-                    # all_reduce_mean
+                with opr_priority_scope(cg, -(2 ** 30)):
+                    # always run all_reduce_mean first except add_update
                     grad = (
                         all_reduce_sum(grad, "grad_" + str(get_group_id()))
                         / get_world_size()
                     )
-                with opr_priority_scope(cg, (1 << 30) - priority):
+                with opr_priority_scope(cg, -(2 ** 31)):
+                    # always run add_update first
                     grad_update = add_update(param.grad, grad)
             else:
                 grad_update = add_update(param.grad, grad)

python_module/src/cpp/megbrain_config.cpp

@@ -66,6 +66,8 @@ bool _config::set_comp_graph_option(
     SET_CG_OPTION(graph_opt.jit);
     SET_CG_OPTION(graph_opt.tensorrt);
     SET_CG_OPTION(graph_opt_level);
+    SET_CG_OPTION(allreduce_pack_max_size);
+    SET_CG_OPTION(allreduce_pack_ignore_first);
     SET_CG_OPTION(var_sanity_check_first_run);
     SET_CG_OPTION(no_profiling_on_shape_change);
     SET_CG_OPTION(allocate_static_mem_after_graph_compile);

python_module/src/swig/comp_graph_tools.i

@@ -1,3 +1,7 @@
+%{
+#include "megbrain/gopt/framework.h"
+%}
+
 %inline {
 
     SymbolVarArray _get_owner_opr_inputs(SymbolVar var) {
@@ -35,5 +39,17 @@
         }
         return mgb::cg::replace_oprs(vars, oprmap);
     }
+
+    void _set_priority_to_id(const SymbolVarArray& dest_vars) {
+        auto on_opr = [](mgb::cg::OperatorNodeBase* opr) {
+            if (opr->node_prop().attribute().priority == 0) {
+                opr->node_prop().attribute().priority = opr->id();
+            }
+        };
+        mgb::cg::DepOprIter dep_iter{on_opr};
+        for (const SymbolVar& var : dest_vars) {
+            dep_iter.add(var);
+        }
+    }
 }
 // vim: ft=swig foldmethod=marker foldmarker=f{{{,f}}}

src/core/impl/graph/cg_impl.cpp

@@ -441,12 +441,22 @@ ComputingGraphImpl::CompileState ComputingGraphImpl::compile_prepare(
         optimizer.verbosity(options().log_level);
         optimizer.enable_check_result(options().graph_opt_level < 0);
         if (sopr_stat.has_virtual_grad) {
-            if (need_opt)
+            if (need_opt) {
+#if MGB_ENABLE_OPR_MM
+                optimizer.add_pass<gopt::PackAllReduceScanPass>();
+#endif
                 optimizer.add_preset_passes(false, nullptr, &options());
+            }
             optimizer.add_pass<gopt::ExpandVirtualGradPass>();
         }
-        if (need_opt)
+        if (need_opt) {
             optimizer.add_preset_passes(true, nullptr, &options());
+#if MGB_ENABLE_OPR_MM
+            if (sopr_stat.has_virtual_grad) {
+                optimizer.add_pass<gopt::PackAllReduceReplacePass>();
+            }
+#endif
+        }
         optimizer.apply_inplace(dest_vars);
     }
 #endif

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

@@ -327,6 +327,18 @@ class ComputingGraph : public std::enable_shared_from_this<ComputingGraph>,
              */
             int16_t graph_opt_level = 2;
 
+            /*!
+             * max size of allreduce packs in MB
+             * set this option to zero to disable PackAllReducePass
+             */
+            int16_t allreduce_pack_max_size = 0;
+
+            /*!
+             * do not pack the first n allreduces
+             * PackAllReducePass disabled if allreduce_pack_max_size is zero
+             */
+            int16_t allreduce_pack_ignore_first = 2;
+
             /*!
              * set logging level, larger number means more verbose
              * 0: no log info

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

@@ -183,7 +183,6 @@ SymbolVarArray replace_oprs(
 SymbolVarArray replace_vars_comp_graph(
     const SymbolVarArray &dest, ComputingGraph* new_graph);
 
-
 SymbolVarArray find_h2d(const SymbolVarArray& dest);
 
 /*!

src/gopt/impl/misc.cpp

@@ -17,6 +17,7 @@
 #include "megbrain/opr/utility.h"
 #include "megbrain/serialization/serializer.h"
 #include "megbrain/serialization/opr_shallow_copy.h"
+#include "../../core/impl/graph/cg_impl.h"
 
 using namespace mgb;
 using namespace gopt;
@@ -657,4 +658,309 @@ void RemoveRedundantTypeCvtPass::apply(OptState& opt) const {
     rewriter.apply_inplace();
 }
 
+#if MGB_ENABLE_OPR_MM
+#include "megbrain/opr/collective_comm.h"
+
+/* ======================= PackAllReduceScanPass ====================== */
+
+const char* PackAllReduceScanPass::name() const {
+    return "pack_allreduce_scan";
+}
+
+void PackAllReduceScanPass::apply(OptState& opt) const {
+    auto comp_graph = opt.graph().comp_graph();
+    if (comp_graph->options().allreduce_pack_max_size == 0) return;
+    auto cb_scan = [this] (OperatorNodeBase* opr) {
+        if (check_pattern(opr)) {
+            auto& comm = opr->cast_final_safe<opr::CollectiveComm>();
+            VarNode* target = comm.input(0)->owner_opr()->input(0);
+            // only pack allreduces of grads of the same target
+            // in case two allreduces depend on each other
+            size_t id = target->id();
+            uint64_t hash = XXHash().update(&id, sizeof(size_t)).digest();
+            comm.set_pack_hash(hash);
+        }
+    };
+    opt.graph().iter(cb_scan);
+}
+
+bool PackAllReduceScanPass::check_pattern(OperatorNodeBase* opr) {
+    if (!opr->same_type<opr::CollectiveComm>()) return false;
+    auto& comm = opr->cast_final_safe<opr::CollectiveComm>();
+    if (comm.param().mode != opr::CollectiveComm::Param::Mode::ALL_REDUCE_SUM) return false;
+    if (comm.input().size() != 1) return false;
+
+    auto grad = comm.input(0)->owner_opr();
+    if (!grad->same_type<opr::VirtualGrad>()) return false;
+    if (grad->input().size() != 2 or grad->output().size() != 1) return false;
+
+    auto param = grad->input(1)->owner_opr();
+    if (!param->same_type<opr::SharedDeviceTensor>() and
+        !param->same_type<opr::VolatileSharedDeviceTensor>()) return false;
+    if (param->input().size() != 0) return false;
+
+    return true;
+}
+
+/* ======================= PackAllReduceReplacePass ====================== */
+
+const char* PackAllReduceReplacePass::name() const {
+    return "pack_allreduce_replace";
+}
+
+class PackAllReduceReplacePass::GroupInfo {
+public:
+    GroupInfo(int _device, DType _dtype,
+            size_t _nr_devices, bool _is_root, int _rank,
+            std::shared_ptr<opr::GroupClient> _group_client,
+            const std::string& _backend);
+
+    uint64_t hash(uint64_t extra) const;
+
+    int device;
+    DType dtype;
+    size_t nr_devices;
+    bool is_root;
+    int rank;
+    std::shared_ptr<opr::GroupClient> group_client;
+    std::string backend;
+};
+
+PackAllReduceReplacePass::GroupInfo::GroupInfo(
+        int _device, DType _dtype,
+        size_t _nr_devices, bool _is_root, int _rank,
+        std::shared_ptr<opr::GroupClient> _group_client,
+        const std::string& _backend) :
+    device(_device), dtype(_dtype),
+    nr_devices(_nr_devices), is_root(_is_root), rank(_rank),
+    group_client(_group_client), backend(_backend) {
+}
+
+uint64_t PackAllReduceReplacePass::GroupInfo::hash(uint64_t extra) const {
+    DTypeEnum ev = dtype.enumv();
+    const std::string& server_addr = group_client->get_addr();
+    return XXHash()
+        .update(&extra, sizeof(uint64_t))
+        .update(&device, sizeof(int))
+        .update(&ev, sizeof(DTypeEnum))
+        .update(&nr_devices, sizeof(size_t))
+        .update(&is_root, sizeof(bool))
+        .update(&rank, sizeof(int))
+        .update(server_addr.c_str(), server_addr.size())
+        .update(backend.c_str(), backend.size())
+        .digest();
+}
+
+uint64_t PackAllReduceReplacePass::collect_groups(OperatorNodeBase* opr,
+        ThinHashMap<uint64_t, std::shared_ptr<GroupInfo>>& group_info,
+        ThinHashMap<uint64_t, cg::OprNodeArray>& groups) {
+    // check CollectiveComm oprs that have been marked in PackAllReduceScanPass
+    if (!opr->same_type<opr::CollectiveComm>()) return 0;
+    opr::CollectiveComm& comm = opr->cast_final_safe<opr::CollectiveComm>();
+    if (comm.pack_hash() == 0) return 0;  // pack_hash not set
+
+    VarNode* var = comm.input(0);
+    auto info = std::make_shared<GroupInfo>(
+        var->comp_node().locator().device,
+        var->dtype(),
+        comm.nr_devices(),
+        comm.is_root(),
+        comm.rank(),
+        comm.group_client(),
+        comm.backend()
+    );
+    uint64_t hash = info->hash(comm.pack_hash());
+    if (group_info.find(hash) == group_info.end()) {
+        group_info.emplace(hash, info);
+    }
+    groups[hash].push_back(opr);
+    return hash;
+}
+
+void PackAllReduceReplacePass::divide_packs(
+        const ThinHashMap<uint64_t, cg::OprNodeArray>& groups,
+        ThinHashMap<uint64_t, std::vector<cg::OprNodeArray>>& packs,
+        size_t max_size) {
+    cg::OprNodeArray pack;
+    size_t sum = 0;
+    for (auto it : groups) {
+        uint64_t hash = it.first;
+        const cg::OprNodeArray& group = it.second;
+        for (size_t i = 0; i < group.size(); i++) {
+            OperatorNodeBase* opr = group[i];
+            VarNode* var = opr->input(0);
+            const TensorShape* shape = var->owner_graph()
+                    ->static_infer_manager().infer_shape_fallible(var);
+            if (shape == nullptr) continue;
+            pack.push_back(opr);
+            sum += var->dtype().size(shape->total_nr_elems());
+            if (sum >= max_size) {
+                if (pack.size() > 1) packs[hash].push_back(pack);
+                pack.clear();
+                sum = 0;
+            }
+        }
+        if (pack.size() > 1) packs[hash].push_back(pack);
+        pack.clear();
+        sum = 0;
+    }
+}
+
+void PackAllReduceReplacePass::insert_packed_oprs(
+        size_t pack_id,
+        const cg::OprNodeArray& pack,
+        std::shared_ptr<GroupInfo> info,
+        ThinHashMap<VarNode*, VarNode*>& replace_map, int priority) {
+    // set priority
+    mgb_assert(pack.size() > 0);
+    auto graph = pack[0]->owner_graph();
+    auto on_opr_inserted = [priority] (const cg::event::OprInserted& event) {
+        event.opr->node_prop().attribute().priority = priority;
+    };
+    auto handler = graph->event().register_receiver<cg::event::OprInserted>(on_opr_inserted);
+
+    // flatten inputs and record shapes and partition
+    std::vector<SymbolVar> shapes;
+    SymbolVarArray flattens;
+    SymbolVarArray partition;
+    for (size_t i = 0; i < pack.size(); i++) {
+        VarNode* var = pack[i]->input(0);
+        auto shape = opr::GetVarShape::make(SymbolVar(var));
+        shapes.push_back(shape);
+        SymbolVar flatten = SymbolVar(var).flatten();
+        flattens.push_back(flatten);
+        partition.push_back(opr::Reduce::make(shape, {opr::Reduce::Mode::PRODUCT, 0}));
+    }
+
+    // concat
+    SymbolVar concat = opr::Concat::make(flattens, 0);
+
+    // allreduce
+    std::string key = ssprintf("grad_pack_%zu", pack_id);
+    auto param = opr::CollectiveComm::Param::Mode::ALL_REDUCE_SUM;
+    SymbolVar allreduce = opr::CollectiveComm::make({concat}, graph,
+        key, info->nr_devices, info->is_root, info->rank,
+        info->group_client, param, info->dtype, info->backend)[0];
+
+    // split according to recorded partition
+    SymbolVarArray splits = opr::Split::make(allreduce,
+        opr::Split::Options::make_partition(0, partition));
+
+    // reshape and insert results into replace_map
+    mgb_assert(pack.size() == splits.size());
+    for (size_t i = 0; i < pack.size(); i++) {
+        VarNode* reshape = splits[i].reshape(shapes[i]).node();
+        replace_map[pack[i]->output(0)] = reshape;
+    }
+}
+
+void PackAllReduceReplacePass::apply(OptState& opt) const {
+    // get graph options
+    auto comp_graph = opt.graph().comp_graph();
+    size_t max_size = comp_graph->options().allreduce_pack_max_size * 1024 * 1024;
+    size_t ignore_first = comp_graph->options().allreduce_pack_ignore_first;
+    if (max_size == 0) return;
+
+    // get topo order
+    auto& topo_sorter = static_cast<cg::ComputingGraphImpl*>(comp_graph)->topo_sorter();
+    cg::CompSeqExtraInfo extra_info;
+    VarNodeArray endpoints = to_var_node_array(opt.graph().endpoint_vars());
+    const cg::OprNodeArray* seq = topo_sorter.get_comp_seq(extra_info, endpoints);
+    topo_sorter.restore_opr_prop();
+
+    // collect allreduce groups from topo sequence
+    ThinHashMap<uint64_t, std::shared_ptr<GroupInfo>> group_info;
+    ThinHashMap<uint64_t, cg::OprNodeArray> groups;
+    for (size_t i = 0; i < seq->size(); i++) {
+        if (seq->at(i)->same_type<opr::CollectiveComm>()) {
+            // ignore the first several allreduces
+            if (ignore_first > 0) {
+                --ignore_first;
+            } else {
+                collect_groups(seq->at(i), group_info, groups);
+            }
+        }
+    }
+
+    // divide groups into packs
+    ThinHashMap<uint64_t, std::vector<cg::OprNodeArray>> packs;
+    divide_packs(groups, packs, max_size);
+
+    // make sure that oprs inserted in this pass (reshape, concat, allreduce,
+    // split, reshape) have higher priority than existing operators
+    int priority = -seq->size() - 100;
+
+    // insert packed operators and generate replace_map
+    ThinHashMap<VarNode*, VarNode*> replace_map;
+    size_t pack_id = 0;
+    for (auto it : packs) {
+        uint64_t hash = it.first;
+        for (auto pack : it.second) {
+            opt.call_with_opr(pack[0], [&]() {
+                insert_packed_oprs(pack_id, pack, group_info[hash], replace_map, priority);
+            }, OprPropertyFlag::NONE);
+            pack_id += 1;
+        }
+    }
+
+    // replace vars
+    auto rewriter = opt.graph().make_rewriter();
+    auto cb_replace = [&](OperatorNodeBase* opr) {
+        for (auto i : opr->input()) {
+            auto iter = replace_map.find(i);
+            if (iter != replace_map.end()) {
+                rewriter.replace_var(i, iter->second, nullptr);
+            }
+        }
+        rewriter.auto_replace_outputs(opr);
+    };
+    opt.graph().iter(cb_replace);
+    rewriter.apply_inplace();
+}
+
+#else
+
+/* ======================= PackAllReduceScanPass ====================== */
+
+const char* PackAllReduceScanPass::name() const {
+    return "pack_allreduce_scan";
+}
+
+void PackAllReduceScanPass::apply(OptState& opt) const {
+}
+
+bool PackAllReduceScanPass::check_pattern(OperatorNodeBase* opr) {
+    return true;
+}
+
+/* ======================= PackAllReduceReplacePass ====================== */
+
+const char* PackAllReduceReplacePass::name() const {
+    return "pack_allreduce_replace";
+}
+
+void PackAllReduceReplacePass::apply(OptState& opt) const {}
+
+uint64_t PackAllReduceReplacePass::collect_groups(
+        OperatorNodeBase* opr,
+        ThinHashMap<uint64_t, std::shared_ptr<GroupInfo>>& group_info,
+        ThinHashMap<uint64_t, cg::OprNodeArray>& groups) {
+    return 0;
+}
+
+void PackAllReduceReplacePass::divide_packs(
+        const ThinHashMap<uint64_t, cg::OprNodeArray>& groups,
+        ThinHashMap<uint64_t, std::vector<cg::OprNodeArray>>& packs,
+        size_t max_size) {
+}
+
+void PackAllReduceReplacePass::insert_packed_oprs(
+        size_t pack_id,
+        const cg::OprNodeArray& pack,
+        std::shared_ptr<GroupInfo> info,
+        ThinHashMap<VarNode*, VarNode*>& replace_map, int priority) {
+}
+
+#endif  // MGB_ENABLE_OPR_MM
+
 // vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}}

src/gopt/include/megbrain/gopt/misc.h

@@ -11,6 +11,8 @@
 
 #pragma once
 
+#include <vector>
+
 #include "megbrain/gopt/framework.h"
 
 namespace mgb {
@@ -90,6 +92,45 @@ namespace gopt {
         void apply(OptState& opt) const override;
     };
 
+    //! scan allreduces of param grads
+    class PackAllReduceScanPass final : public Pass {
+    public:
+        const char* name() const override;
+        void apply(OptState& opt) const override;
+
+    private:
+        // check pattern param -> grad -> allreduce
+        static bool check_pattern(OperatorNodeBase* opr);
+    };
+
+    //! pack allreduces of param grads
+    class PackAllReduceReplacePass final : public Pass {
+    public:
+        class GroupInfo;
+
+        const char* name() const override;
+        void apply(OptState& opt) const override;
+
+        // collect allreduces and divide into groups
+        static uint64_t collect_groups(
+                OperatorNodeBase* opr,
+                ThinHashMap<uint64_t, std::shared_ptr<GroupInfo>>& group_info,
+                ThinHashMap<uint64_t, cg::OprNodeArray>& groups);
+
+        // divide groups into packs, max_size in MB
+        static void divide_packs(
+                const ThinHashMap<uint64_t, cg::OprNodeArray>& groups,
+                ThinHashMap<uint64_t, std::vector<cg::OprNodeArray>>& packs,
+                size_t max_size);
+
+        // insert packed operators and update replace_map
+        static void insert_packed_oprs(
+                size_t pack_id,
+                const cg::OprNodeArray& pack,
+                std::shared_ptr<GroupInfo> info,
+                ThinHashMap<VarNode*, VarNode*>& replace_map, int priority);
+    };
+
 } // namespace gopt
 } // namespace mgb
 

src/gopt/test/misc.cpp

@@ -14,6 +14,7 @@
 #include "megbrain/gopt/basic_arith.h"
 #include "megbrain/gopt/misc.h"
 #include "megbrain/opr/basic_arith_wrapper.h"
+#include "megbrain/opr/blas.h"
 #include "megbrain/opr/cond.h"
 #include "megbrain/opr/tensor_manip.h"
 #include "megbrain/opr/utility.h"
@@ -410,4 +411,322 @@ TEST_PASS(RemoveRedundantTypeCvtPass, Basic) {
     check(x_q8_q8, x_q8_fp32_q8_);
 }
 
+#if MGB_ENABLE_OPR_MM
+#include "megbrain/opr/collective_comm.h"
+#include "../../opr-mm/test/mock_client.h"
+
+TEST_PASS(PackAllReduceScanPass, Basic) {
+    auto graph = ComputingGraph::make();
+    graph->options().allreduce_pack_max_size = 5000;
+
+    auto client = std::make_shared<test::MockGroupClient>();
+    auto cn = CompNode::load("gpux");
+
+    auto dev_x0 = std::make_shared<DeviceTensorND>(cn, TensorShape{3, 5});
+    auto dev_x1 = std::make_shared<DeviceTensorND>(cn, TensorShape{4, 6});
+    auto dev_y0 = std::make_shared<DeviceTensorND>(cn, TensorShape{1});
+    auto dev_y1 = std::make_shared<DeviceTensorND>(cn, TensorShape{1});
+
+    auto x0 = opr::SharedDeviceTensor::make(*graph, dev_x0);
+    auto x1 = opr::VolatileSharedDeviceTensor::make(*graph, dev_x1);
+    auto y0 = opr::SharedDeviceTensor::make(*graph, dev_y0);
+    auto y1 = opr::VolatileSharedDeviceTensor::make(*graph, dev_y1);
+
+    auto grad0 = opr::VirtualGrad::make(y0, x0);
+    auto grad1 = opr::VirtualGrad::make(y0, x1);
+    auto grad2 = opr::VirtualGrad::make(y1, x0);
+    auto grad3 = opr::VirtualGrad::make(y1, x1);
+
+    auto mode = opr::CollectiveComm::Param::Mode::ALL_REDUCE_SUM;
+    auto comm0 = opr::CollectiveComm::make({grad0}, graph.get(),
+        "grad0", 2, 0, 0, client, mode)[0];
+    auto comm1 = opr::CollectiveComm::make({grad1}, graph.get(),
+        "grad1", 2, 0, 0, client, mode)[0];
+    auto comm2 = opr::CollectiveComm::make({grad2}, graph.get(),
+        "grad2", 2, 0, 0, client, mode)[0];
+    auto comm3 = opr::CollectiveComm::make({grad3}, graph.get(),
+        "grad3", 2, 0, 0, client, mode)[0];
+
+    gopt::GraphOptimizer()
+        .add_pass<gopt::PackAllReduceScanPass>()
+        .apply({{comm0, comm1, comm2, comm3}});
+
+    auto get_hash = [] (const SymbolVar& symvar) {
+        cg::OperatorNodeBase* opr = symvar.node()->owner_opr();
+        return opr->cast_final_safe<opr::CollectiveComm>().pack_hash();
+    };
+    uint64_t hash0 = get_hash(comm0);
+    uint64_t hash1 = get_hash(comm1);
+    uint64_t hash2 = get_hash(comm2);
+    uint64_t hash3 = get_hash(comm3);
+
+    ASSERT_EQ(hash0, hash1);
+    ASSERT_EQ(hash2, hash3);
+    ASSERT_NE(hash0, hash2);
+}
+
+TEST_PASS(PackAllReduceReplacePass, CollectGroups) {
+    REQUIRE_GPU(2);
+    auto cns = load_multiple_xpus(2);
+    auto graph = ComputingGraph::make();
+    graph->options().graph_opt_level = 2;
+
+    auto cli0 = std::make_shared<test::MockGroupClient>("mock_addr0");
+    auto cli1 = std::make_shared<test::MockGroupClient>("mock_addr1");
+
+    using GroupInfo = gopt::PackAllReduceReplacePass::GroupInfo;
+    ThinHashMap<uint64_t, std::shared_ptr<GroupInfo>> group_info;
+    ThinHashMap<uint64_t, cg::OprNodeArray> groups;
+
+    auto add_opr = [&] (const CompNode& cn, TensorShape shape, const DType& dt,
+        std::shared_ptr<test::MockGroupClient> client, uint64_t extra_hash) {
+        auto dev0 = std::make_shared<DeviceTensorND>(cn, shape, dt);
+        auto wrt = opr::SharedDeviceTensor::make(*graph, dev0);
+
+        auto dev1 = std::make_shared<DeviceTensorND>(cn, TensorShape{1}, dt);
+        auto target = opr::SharedDeviceTensor::make(*graph, dev1);
+
+        auto grad = opr::VirtualGrad::make(target, wrt);
+
+        auto comm = opr::CollectiveComm::make(
+            {grad}, graph.get(), "key", 2, 0, 0, client,
+            opr::CollectiveComm::Param::Mode::ALL_REDUCE_SUM)[0]
+            .node()->owner_opr();
+
+        comm->cast_final_safe<opr::CollectiveComm>().set_pack_hash(extra_hash);
+
+        return gopt::PackAllReduceReplacePass::collect_groups(comm, group_info, groups);
+    };
+
+    uint64_t hash0 = add_opr(cns[0], TensorShape{1, 3}, dtype::Float32{}, cli0, 1);
+    uint64_t hash1 = add_opr(cns[0], TensorShape{2, 4}, dtype::Float32{}, cli0, 1);  // same
+    uint64_t hash2 = add_opr(cns[1], TensorShape{3, 5}, dtype::Float32{}, cli0, 1);  // comp_node
+    uint64_t hash3 = add_opr(cns[0], TensorShape{4, 6}, dtype::Float16{}, cli0, 1);  // dtype
+    uint64_t hash4 = add_opr(cns[0], TensorShape{5, 7}, dtype::Float32{}, cli1, 1);  // client
+    uint64_t hash5 = add_opr(cns[0], TensorShape{6, 8}, dtype::Float32{}, cli0, 2);  // extra_hash
+
+    ASSERT_EQ(hash0, hash1);
+
+    std::set<uint64_t> s;
+    s.insert(hash0);
+    s.insert(hash1);
+    s.insert(hash2);
+    s.insert(hash3);
+    s.insert(hash4);
+    s.insert(hash5);
+    ASSERT_EQ(5, s.size());
+
+    ASSERT_EQ(1, group_info.count(hash0));
+    ASSERT_EQ(1, group_info.count(hash1));
+    ASSERT_EQ(1, group_info.count(hash2));
+    ASSERT_EQ(1, group_info.count(hash3));
+    ASSERT_EQ(1, group_info.count(hash4));
+    ASSERT_EQ(1, group_info.count(hash5));
+
+    ASSERT_EQ(2, groups[hash0].size());
+    ASSERT_EQ(2, groups[hash1].size());
+    ASSERT_EQ(1, groups[hash2].size());
+    ASSERT_EQ(1, groups[hash3].size());
+    ASSERT_EQ(1, groups[hash4].size());
+    ASSERT_EQ(1, groups[hash5].size());
+}
+
+TEST_PASS(PackAllReduceReplacePass, DividePacks) {
+    auto cn = CompNode::load("gpux");
+    auto graph = ComputingGraph::make();
+    auto client = std::make_shared<test::MockGroupClient>();
+    auto mode = opr::CollectiveComm::Param::Mode::ALL_REDUCE_SUM;
+
+    ThinHashMap<uint64_t, cg::OprNodeArray> groups;
+    ThinHashMap<uint64_t, std::vector<cg::OprNodeArray>> packs;
+
+    auto insert_opr = [&] (size_t size) {
+        auto dev = std::make_shared<DeviceTensorND>(cn, TensorShape{size / sizeof(float)});
+        auto sd = opr::SharedDeviceTensor::make(*graph, dev);
+        auto symvar = opr::CollectiveComm::make({sd}, graph.get(),
+            "key", 2, 0, 0, client, mode)[0];
+        auto opr = symvar.node()->owner_opr();
+        auto& comm = opr->cast_final_safe<opr::CollectiveComm>();
+        comm.set_pack_hash(1);
+        return opr;
+    };
+
+    auto pack_size = [&] (cg::OprNodeArray& pack) {
+        size_t sum = 0;
+        for (size_t i = 0; i < pack.size(); i++) {
+            auto var = pack[i]->input(0);
+            sum += var->dtype().size(var->shape().total_nr_elems());
+        }
+        return sum;
+    };
+
+    groups[0].push_back(insert_opr(100));  // group0, pack0, size=1100
+    groups[0].push_back(insert_opr(300));  // group0, pack0, size=1100
+    groups[0].push_back(insert_opr(400));  // group0, pack0, size=1100
+    groups[0].push_back(insert_opr(300));  // group0, pack0, size=1100
+    groups[0].push_back(insert_opr(500));  // group0, pack1, size=800
+    groups[0].push_back(insert_opr(200));  // group0, pack1, size=800
+    groups[0].push_back(insert_opr(100));  // group0, pack1, size=800
+
+    groups[1].push_back(insert_opr(100));  // group1, pack0, size=900
+    groups[1].push_back(insert_opr(400));  // group1, pack0, size=900
+    groups[1].push_back(insert_opr(300));  // group1, pack0, size=900
+    groups[1].push_back(insert_opr(100));  // group1, pack0, size=900
+
+    gopt::PackAllReduceReplacePass::divide_packs(groups, packs, 1000);
+
+    ASSERT_EQ(2, packs.size());
+
+    ASSERT_EQ(2, packs[0].size());
+    ASSERT_EQ(4, packs[0][0].size());
+    ASSERT_EQ(1100, pack_size(packs[0][0]));
+    ASSERT_EQ(3, packs[0][1].size());
+    ASSERT_EQ(800, pack_size(packs[0][1]));
+
+    ASSERT_EQ(1, packs[1].size());
+    ASSERT_EQ(4, packs[1][0].size());
+    ASSERT_EQ(900, pack_size(packs[1][0]));
+}
+
+TEST_PASS(PackAllReduceReplacePass, InsertPackedOprs) {
+    auto cn = CompNode::load("gpux");
+    auto graph = ComputingGraph::make();
+    auto client = std::make_shared<test::MockGroupClient>();
+    auto mode = opr::CollectiveComm::Param::Mode::ALL_REDUCE_SUM;
+
+    size_t nr_devices = 2;
+    uint32_t rank = 0;
+    uint32_t root = 0;
+
+    using GroupInfo = gopt::PackAllReduceReplacePass::GroupInfo;
+    ThinHashMap<uint64_t, std::shared_ptr<GroupInfo>> group_info;
+    ThinHashMap<uint64_t, cg::OprNodeArray> groups;
+
+    auto insert_opr = [&] (const TensorShape& shape) {
+        auto dev = std::make_shared<DeviceTensorND>(cn, shape);
+        auto sd = opr::SharedDeviceTensor::make(*graph, dev);
+        auto symvar = opr::CollectiveComm::make({sd}, graph.get(),
+            "key", nr_devices, rank, root, client, mode)[0];
+        auto opr = symvar.node()->owner_opr();
+        auto& comm = opr->cast_final_safe<opr::CollectiveComm>();
+        comm.set_pack_hash(1);
+        gopt::PackAllReduceReplacePass::collect_groups(opr, group_info, groups);
+        return symvar;
+    };
+
+    auto shape_x = TensorShape{100, 200};
+    auto shape_y = TensorShape{200, 400};
+
+    auto x = insert_opr(shape_x);
+    auto y = insert_opr(shape_y);
+
+    ASSERT_EQ(1, group_info.size());
+    ASSERT_EQ(1, groups.size());
+    auto info = group_info.begin()->second;
+    auto pack = groups.begin()->second;
+    size_t pack_id = 0;
+    ThinHashMap<VarNode*, VarNode*> replace_map;
+    gopt::PackAllReduceReplacePass::insert_packed_oprs(pack_id, pack, info, replace_map, -1);
+
+    auto grad_x = SymbolVar(x.node()->owner_opr()->input(0));
+    auto grad_y = SymbolVar(y.node()->owner_opr()->input(0));
+
+    auto concat = opr::Concat::make({grad_x.flatten(), grad_y.flatten()}, 0);
+
+    std::string key = ssprintf("grad_pack_%zu", pack_id);
+    auto allreduce = opr::CollectiveComm::make({concat}, graph.get(),
+        key, nr_devices, rank, root, client, mode)[0];
+
+    std::vector<size_t> partition;
+    partition.push_back(shape_x.total_nr_elems());
+    partition.push_back(shape_y.total_nr_elems());
+    auto splits = opr::Split::make(allreduce,
+        opr::Split::Options::make_partition(allreduce, 0, partition));
+
+    ASSERT_EQ(2, splits.size());
+    auto dest_x = splits[0].reshape(shape_x);
+    auto dest_y = splits[1].reshape(shape_y);
+
+    ASSERT_EQ(2, replace_map.size());
+
+    ASSERT_TRUE(replace_map.count(x.node()) > 0);
+    ASSERT_EQ(replace_map.at(x.node()), dest_x.node());
+
+    ASSERT_TRUE(replace_map.count(y.node()) > 0);
+    ASSERT_EQ(replace_map.at(y.node()), dest_y.node());
+}
+
+TEST_PASS(PackAllReduceReplacePass, Equivalence) {
+    REQUIRE_GPU(2);
+    auto cns = load_multiple_xpus(2);
+    auto client = std::make_shared<test::MockGroupClient>();
+
+    auto build_graph = [&] (uint32_t rank, std::shared_ptr<ComputingGraph> graph,
+                            SymbolVarArray& array) {
+        HostTensorGenerator<> gen;
+        auto cn = cns[rank];
+        auto host_x = gen({1, 1000});
+        auto host_y = gen({1000, 1});
+
+        auto dev_x = std::make_shared<DeviceTensorND>(cn);
+        auto dev_y = std::make_shared<DeviceTensorND>(cn);
+
+        dev_x->copy_from(*host_x).sync();
+        dev_y->copy_from(*host_y).sync();
+
+        auto x = opr::SharedDeviceTensor::make(*graph, dev_x);
+        auto y = opr::VolatileSharedDeviceTensor::make(*graph, dev_y);
+        auto loss = opr::MatrixMul::make(x, y).flatten();
+
+        auto grad_x = opr::VirtualGrad::make(loss, x);
+        auto grad_y = opr::VirtualGrad::make(loss, y);
+
+        using Mode = opr::CollectiveComm::Param::Mode;
+        bool is_root = (rank == 0);
+        auto reduced_x = opr::CollectiveComm::make({grad_x}, graph.get(),
+            "x", 2, is_root, rank, client, Mode::ALL_REDUCE_SUM)[0] / 2;
+        auto reduced_y = opr::CollectiveComm::make({grad_y}, graph.get(),
+            "y", 2, is_root, rank, client, Mode::ALL_REDUCE_SUM)[0] / 2;
+
+        graph->options().allreduce_pack_max_size = 5000;
+        graph->options().allreduce_pack_ignore_first = 0;
+
+        auto dest_vars = gopt::GraphOptimizer{}
+            .add_pass<gopt::PackAllReduceScanPass>()
+            .add_pass<gopt::PackAllReduceReplacePass>()
+            .apply({{reduced_x, reduced_y}}).endpoint_vars();
+
+        array.emplace_back(reduced_x);
+        array.emplace_back(reduced_y);
+        array.emplace_back(dest_vars[0]);
+        array.emplace_back(dest_vars[1]);
+    };
+
+    auto run = [&] (uint32_t rank) {
+        auto graph = ComputingGraph::make();
+        SymbolVarArray array;
+        build_graph(rank, graph, array);
+
+        HostTensorND host_reduced_x, host_reduced_y, host_dest_0, host_dest_1;
+
+        graph->options().allreduce_pack_max_size = 0;
+        auto func = graph->compile({make_callback_copy(array[0], host_reduced_x),
+                                    make_callback_copy(array[1], host_reduced_y),
+                                    make_callback_copy(array[2], host_dest_0),
+                                    make_callback_copy(array[3], host_dest_1)});
+        func->execute();
+
+        MGB_ASSERT_TENSOR_EQ(host_reduced_x, host_dest_0);
+        MGB_ASSERT_TENSOR_EQ(host_reduced_y, host_dest_1);
+    };
+
+    std::thread t0(run, 0);
+    std::thread t1(run, 1);
+
+    t0.join();
+    t1.join();
+}
+
+#endif  // MGB_ENABLE_OPR_MM
+
 // vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}}

src/opr-mm/impl/collective_comm.cpp

@@ -461,16 +461,7 @@ void CollectiveComm::opr_register() {
     m_rank = reg_info.rank;
     m_root = reg_info.root_rank;
 
-    MegRayCommunicatorBuilder* builder;
-
-    {
-        static std::mutex user_data_mtx;
-        std::unique_lock<std::mutex> lk(user_data_mtx);
-        builder = owner_graph()->options().user_data
-                .get_user_data_or_create<MegRayCommunicatorBuilder>();
-    }
-
-    m_megray_comm = builder->get_megray_comm(
+    m_megray_comm = MegRayCommBuilder::get_megray_comm(
             reg_info.hash, m_key, m_nr_devices, m_rank,
             get_megray_backend(m_backend), m_group_client);
 
@@ -736,13 +727,15 @@ cg::OperatorNodeBase* opr_shallow_copy_collective_mm(
         const cg::OperatorNodeBase& opr_, const VarNodeArray& inputs,
         const OperatorNodeConfig& config) {
     auto&& opr = opr_.cast_final_safe<opr::CollectiveComm>();
-    return opr::CollectiveComm::make(
+    auto new_opr = CollectiveComm::make(
                    to_symbol_var_array(inputs), ctx.owner_graph(opr_, inputs),
                    opr.key(), opr.nr_devices(), opr.is_root(), opr.rank(),
                    opr.group_client(), opr.dev_buffers(), opr.param(),
                    opr.dtype(), opr.backend(), config)[0]
             .node()
             ->owner_opr();
+    new_opr->cast_final_safe<opr::CollectiveComm>().set_pack_hash(opr.pack_hash());
+    return new_opr;
 }
 MGB_REG_OPR_SHALLOW_COPY(CollectiveComm, opr_shallow_copy_collective_mm);
 

src/opr-mm/impl/io_remote.cpp

@@ -54,13 +54,7 @@ void RemoteSend::scn_do_execute() {
         auto reg_info = m_group_client->opr_register(m_peer.key, 2, 0, false,
                 comp_node.get_uid());
 
-        auto megray_comm_builder =
-                owner_graph()
-                        ->options()
-                        .user_data
-                        .get_user_data_or_create<MegRayCommunicatorBuilder>();
-
-        m_megray_comm = megray_comm_builder->get_megray_comm(
+        m_megray_comm = MegRayCommBuilder::get_megray_comm(
                 reg_info.hash, m_peer.key, 2, 0, MegRay::MEGRAY_UCX, m_group_client);
         m_init = true;
     }
@@ -158,13 +152,7 @@ void RemoteRecv::scn_do_execute() {
                 m_peer.key, 2, false, 1,
                 comp_node.get_uid());
 
-        auto megray_comm_builder =
-                owner_graph()
-                        ->options()
-                        .user_data
-                        .get_user_data_or_create<MegRayCommunicatorBuilder>();
-
-        m_megray_comm = megray_comm_builder->get_megray_comm(
+        m_megray_comm = MegRayCommBuilder::get_megray_comm(
                 reg_info.hash, m_peer.key, 2, 1, MegRay::MEGRAY_UCX, m_group_client);
         m_init = true;
     }

src/opr-mm/impl/megray_helper.cpp

@@ -14,8 +14,8 @@
 using namespace mgb;
 using namespace opr;
 
-bool MegRayCommunicatorBuilder::find(uint64_t hash, std::shared_ptr<MegRay::Communicator>& comm) {
-    std::unique_lock<std::mutex> lk(m_mtx);
+bool MegRayCommBuilder::find(uint64_t hash, std::shared_ptr<MegRay::Communicator>& comm) {
+    std::unique_lock<std::mutex> lk(m_map_mtx);
     auto it = m_megray_comms.find(hash);
     if (it != m_megray_comms.end()) {
         comm = it->second;
@@ -24,27 +24,37 @@ bool MegRayCommunicatorBuilder::find(uint64_t hash, std::shared_ptr<MegRay::Comm
     return false;
 }
 
-void MegRayCommunicatorBuilder::emplace(uint64_t hash,
+void MegRayCommBuilder::emplace(uint64_t hash,
         std::shared_ptr<MegRay::Communicator> comm) {
-    std::unique_lock<std::mutex> lk(m_mtx);
+    std::unique_lock<std::mutex> lk(m_map_mtx);
     m_megray_comms.emplace(hash, comm);
 }
 
-std::shared_ptr<MegRay::Communicator> MegRayCommunicatorBuilder::get_megray_comm(
+std::shared_ptr<MegRay::Communicator> MegRayCommBuilder::get_megray_comm(
         uint64_t hash, std::string key, uint32_t size, uint32_t rank,
         MegRay::Backend backend,
         std::shared_ptr<mgb::opr::GroupClient> group_client) {
+    {
+        // singleton pattern
+        std::unique_lock<std::mutex> lk(sm_instance_mtx);
+        if (sm_instance == nullptr) {
+            sm_instance = new MegRayCommBuilder();
+        }
+    }
+
     std::shared_ptr<MegRay::Communicator> comm;
-    if (!find(hash, comm)) {
+    if (!sm_instance->find(hash, comm)) {
         comm = MegRay::get_communicator(size, rank, backend);
         auto uid = comm->get_uid();
         auto uids = group_client->gather_uid(uid, key, size, rank);
         mgb_assert(comm->init(uids) == MegRay::Status::MEGRAY_OK);
-        emplace(hash, comm);
+        sm_instance->emplace(hash, comm);
     }
     return comm;
 }
 
-MGB_TYPEINFO_OBJ_IMPL(MegRayCommunicatorBuilder);
+MegRayCommBuilder* MegRayCommBuilder::sm_instance = nullptr;
+
+std::mutex MegRayCommBuilder::sm_instance_mtx;
 
 // vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}}

src/opr-mm/include/megbrain/opr/collective_comm.h

@@ -81,6 +81,10 @@ public:
         return m_group_client;
     }
 
+    void set_pack_hash(uint64_t hash) { m_pack_hash = hash; }
+
+    uint64_t pack_hash() const { return m_pack_hash; }
+
     std::shared_ptr<MegRay::Context> megray_ctx() const {
         return m_megray_ctx;
     }
@@ -123,6 +127,9 @@ private:
     // whose shape infer should be disabled *during* static infer phase.
     bool m_enable_shape_infer = false;
 
+    //! set in PackAllReduceScanPass and used in PackAllReduceReplacePass
+    uint64_t m_pack_hash = 0;
+
     std::shared_ptr<MegRay::Context> m_megray_ctx;
     std::shared_ptr<MegRay::Communicator> m_megray_comm;
     bool m_init = false;

src/opr-mm/include/megbrain/opr/group_manager.h

@@ -126,6 +126,8 @@ class GroupClient {
         virtual ~GroupClient() = default;
     
     public:
+        virtual const std::string& get_addr() const = 0;
+
         virtual GroupManager::RegisterInfo opr_register(const std::string& key,
                                                         size_t nr_devices,
                                                         bool is_root, int rank,

src/opr-mm/include/megbrain/opr/megray_helper.h

@@ -23,18 +23,19 @@ namespace opr {
 /*!
  * gather MegRay unique ids and build communicator, use hash for deduplication
  */
-class MegRayCommunicatorBuilder final : public mgb::UserDataContainer::UserData {
-    MGB_TYPEINFO_OBJ_DECL;
-
+class MegRayCommBuilder {
     private:
         bool find(uint64_t hash, std::shared_ptr<MegRay::Communicator>& comm);
         void emplace(uint64_t hash, std::shared_ptr<MegRay::Communicator> comm);
 
         std::unordered_map<uint64_t, std::shared_ptr<MegRay::Communicator>> m_megray_comms;
-        std::mutex m_mtx;
+        std::mutex m_map_mtx;
+
+        static MegRayCommBuilder* sm_instance;
+        static std::mutex sm_instance_mtx;
 
     public:
-        std::shared_ptr<MegRay::Communicator> get_megray_comm(
+        static std::shared_ptr<MegRay::Communicator> get_megray_comm(
                 uint64_t hash, std::string key, uint32_t size, uint32_t rank,
                 MegRay::Backend backend,
                 std::shared_ptr<mgb::opr::GroupClient> group_client);

src/opr-mm/include/megbrain/opr/mm_handler.h

@@ -47,7 +47,7 @@ public:
 
     uint32_t group_barrier(uint32_t size, uint32_t rank) override;
 
-    const std::string& get_addr() const {
+    const std::string& get_addr() const override {
         return m_addr;
     }
 

src/opr-mm/test/collective_comm.cpp

@@ -17,11 +17,10 @@
 #include "megbrain/opr/utility.h"
 #include "megbrain/test/helper.h"
 #include "megbrain/graph.h"
+#include "mock_client.h"
 
 using namespace mgb;
 
-namespace {
-
 using Mode = opr::CollectiveComm::Param::Mode;
 
 SymbolVar make_all_reduce_output(const Mode mode,
@@ -41,41 +40,6 @@ SymbolVarArray make_reduce_scatter_sum_output(const SymbolVarArray& inputs) {
             rdc, opr::Split::Options::make_average(0, inputs.size()));
 }
 
-class MockGroupClient final : public opr::GroupClient {
-    public:
-        ~MockGroupClient() override = default;
-
-        opr::GroupManager::RegisterInfo opr_register(const std::string& key,
-                                                     size_t nr_devices,
-                                                     bool is_root, int rank,
-                                                     uintptr_t stream) {
-            return m_mgr.opr_register(key, nr_devices, is_root, rank, stream);
-        }
-
-        std::vector<std::string> gather_uid(const std::string& uid,
-                const std::string& key, uint32_t size, uint32_t rank) {
-            return m_mgr.gather_uid(uid, key, size, rank);
-        }
-
-        void set_output_shape(const std::string& key,
-                              const TensorShape& shape) override {
-            m_mgr.set_output_shape(key, shape);
-        }
-    
-        TensorShape get_output_shape(const std::string& key) override {
-            return m_mgr.get_output_shape(key);
-        }
-
-        uint32_t group_barrier(uint32_t size, uint32_t rank) override {
-            return m_mgr.group_barrier(size, rank);
-        }
-    
-    private:
-        opr::GroupManager m_mgr;
-};
-
-}  // namespace
-
 TEST(TestOprCollectiveComm, AllReduce) {
     REQUIRE_GPU(2);
 
@@ -88,7 +52,7 @@ TEST(TestOprCollectiveComm, AllReduce) {
         auto host_x1 = gen({28, 28});
         HostTensorND host_y0, host_y1, host_y_expect;
     
-        auto client = std::make_shared<MockGroupClient>();
+        auto client = std::make_shared<test::MockGroupClient>();
         auto graph = ComputingGraph::make();
     
         auto x0 = opr::Host2DeviceCopy::make(*graph, host_x0, cn0);
@@ -126,7 +90,7 @@ TEST(TestOprCollectiveComm, AllReduceMultiThread) {
         auto host_x1 = gen({28, 28});
         HostTensorND host_y0, host_y1, host_y_expect;
 
-        auto client = std::make_shared<MockGroupClient>();
+        auto client = std::make_shared<test::MockGroupClient>();
 
         auto run_0 = [&]() {
             auto graph0 = ComputingGraph::make();
@@ -187,7 +151,7 @@ TEST(TestOprCollectiveComm, AllReduceWithGrad) {
     HostTensorND host_y0, host_y1, host_y_expect;
     HostTensorND host_out_grad0, host_out_grad1, host_out_grad_expect;
 
-    auto client = std::make_shared<MockGroupClient>();
+    auto client = std::make_shared<test::MockGroupClient>();
 
     auto run_0 = [&]() { // rank 0
         auto graph0 = ComputingGraph::make();
@@ -268,7 +232,7 @@ TEST(TestOprCollectiveComm, AllGather) {
     auto host_x1 = gen({28, 28});
     HostTensorND host_y0, host_y1, host_y_expect;
 
-    auto client = std::make_shared<MockGroupClient>();
+    auto client = std::make_shared<test::MockGroupClient>();
     auto graph = ComputingGraph::make();
 
     auto x0 = opr::Host2DeviceCopy::make(*graph, host_x0, cn0);
@@ -300,7 +264,7 @@ TEST(TestOprCollectiveComm, AllGatherMultiThread) {
     auto host_x1 = gen({28, 28});
     HostTensorND host_y0, host_y1, host_y_expect;
 
-    auto client = std::make_shared<MockGroupClient>();
+    auto client = std::make_shared<test::MockGroupClient>();
 
     auto run_0 = [&]() { // rank 0
         auto graph0 = ComputingGraph::make();
@@ -356,7 +320,7 @@ TEST(TestOprCollectiveComm, AllGatherWithGrad) {
     HostTensorND host_out_grad0, host_out_grad1;
     HostTensorND host_out_grad0_expect, host_out_grad1_expect;
 
-    auto client = std::make_shared<MockGroupClient>();
+    auto client = std::make_shared<test::MockGroupClient>();
 
     auto run_0 = [&]() { // rank 0
         auto graph0 = ComputingGraph::make();
@@ -438,7 +402,7 @@ TEST(TestOprCollectiveComm, ReduceScatterSum) {
     auto host_x1 = gen({28, 28});
     HostTensorND host_y0, host_y1, host_y0_expect, host_y1_expect;
 
-    auto client = std::make_shared<MockGroupClient>();
+    auto client = std::make_shared<test::MockGroupClient>();
     auto graph = ComputingGraph::make();
 
     auto x0 = opr::Host2DeviceCopy::make(*graph, host_x0, cn0);
@@ -471,7 +435,7 @@ TEST(TestOprCollectiveComm, ReduceScatterSumMultiThread) {
     auto host_x1 = gen({8});
     HostTensorND host_y0, host_y1, host_y0_expect, host_y1_expect;
 
-    auto client = std::make_shared<MockGroupClient>();
+    auto client = std::make_shared<test::MockGroupClient>();
 
     auto run_0 = [&]() { // rank 0
         auto graph0 = ComputingGraph::make();
@@ -528,7 +492,7 @@ TEST(TestOprCollectiveComm, ReduceScatterSumWithGrad) {
     HostTensorND host_y0, host_y1, host_y0_expect, host_y1_expect;
     HostTensorND host_out_grad0, host_out_grad1, host_out_grad_expect;
 
-    auto client = std::make_shared<MockGroupClient>();
+    auto client = std::make_shared<test::MockGroupClient>();
 
     auto run_0 = [&]() { // rank 0
         auto graph0 = ComputingGraph::make();
@@ -610,7 +574,7 @@ TEST(TestOprCollectiveComm, ReduceSum) {
     auto host_x1 = gen({28, 28});
     HostTensorND host_y0, host_y1, host_y_expect;
 
-    auto client = std::make_shared<MockGroupClient>();
+    auto client = std::make_shared<test::MockGroupClient>();
     auto graph = ComputingGraph::make();
 
     auto x0 = opr::Host2DeviceCopy::make(*graph, host_x0, cn0);
@@ -641,7 +605,7 @@ TEST(TestOprCollectiveComm, ReduceSumMultiThread) {
     auto host_x1 = gen({28, 28});
     HostTensorND host_y0, host_y_expect;
 
-    auto client = std::make_shared<MockGroupClient>();
+    auto client = std::make_shared<test::MockGroupClient>();
 
     auto run_0 = [&]() { // rank 0
         auto graph0 = ComputingGraph::make();
@@ -694,7 +658,7 @@ TEST(TestOprCollectiveComm, ReduceSumWithGrad) {
 
     HostTensorND host_y0, host_y0_expect, host_out_grad0, host_out_grad1;
 
-    auto client = std::make_shared<MockGroupClient>();
+    auto client = std::make_shared<test::MockGroupClient>();
 
     auto run_0 = [&]() { // rank 0
         auto graph0 = ComputingGraph::make();
@@ -764,7 +728,7 @@ TEST(TestOprCollectiveComm, Broadcast) {
     auto host_x0 = gen({28, 28});
     HostTensorND host_y0, host_y1, host_y_expect;
 
-    auto client = std::make_shared<MockGroupClient>();
+    auto client = std::make_shared<test::MockGroupClient>();
     auto graph = ComputingGraph::make();
 
     auto x0 = opr::Host2DeviceCopy::make(*graph, host_x0, cn0);
@@ -794,7 +758,7 @@ TEST(TestOprCollectiveComm, BroadcastMultiThread) {
     auto host_x0 = gen({28, 28});
     HostTensorND host_y0, host_y1;
 
-    auto client = std::make_shared<MockGroupClient>();
+    auto client = std::make_shared<test::MockGroupClient>();
 
     auto run_0 = [&]() { // rank 0
         auto graph0 = ComputingGraph::make();
@@ -840,7 +804,7 @@ TEST(TestOprCollectiveComm, BroadcastWithGrad) {
 
     HostTensorND host_y0, host_y1, host_out_grad, host_out_grad_expect;
 
-    auto client = std::make_shared<MockGroupClient>();
+    auto client = std::make_shared<test::MockGroupClient>();
 
     auto run_0 = [&]() { // rank 0
         auto graph0 = ComputingGraph::make();

src/opr-mm/test/io_remote.cpp

@@ -14,51 +14,14 @@
 #include "megbrain/opr/utility.h"
 #include "megbrain/system.h"
 #include "megbrain/test/helper.h"
+#include "mock_client.h"
 
 #include <thread>
 
 using namespace mgb;
-using namespace opr;
 
-namespace {
-
-class MockGroupClient final : public opr::GroupClient {
-    public:
-        ~MockGroupClient() override = default;
-
-        opr::GroupManager::RegisterInfo opr_register(const std::string& key,
-                                                     size_t nr_devices,
-                                                     bool is_root, int rank,
-                                                     uint64_t comp_node_hash) {
-            return m_mgr.opr_register(key, nr_devices, is_root, rank, comp_node_hash);
-        }
-
-        std::vector<std::string> gather_uid(const std::string& uid,
-                const std::string& key, uint32_t size, uint32_t rank) {
-            return m_mgr.gather_uid(uid, key, size, rank);
-        }
-
-        void set_output_shape(const std::string& key,
-                              const TensorShape& shape) override {
-            m_mgr.set_output_shape(key, shape);
-        }
-
-        TensorShape get_output_shape(const std::string& key) override {
-            return m_mgr.get_output_shape(key);
-        }
-
-        uint32_t group_barrier(uint32_t size, uint32_t rank) override {
-            return m_mgr.group_barrier(size, rank);
-        }
-
-    private:
-        opr::GroupManager m_mgr;
-};
-
-const auto send_tag = RemoteIOBase::Type::SEND;
-const auto recv_tag = RemoteIOBase::Type::RECV;
-
-}  // anonymous namespace
+const auto send_tag = opr::RemoteIOBase::Type::SEND;
+const auto recv_tag = opr::RemoteIOBase::Type::RECV;
 
 TEST(TestOprIORemote, Identity) {
     REQUIRE_GPU(2);
@@ -69,7 +32,7 @@ TEST(TestOprIORemote, Identity) {
     auto host_x = gen({28, 28});
     HostTensorND host_y;
 
-    auto client = std::make_shared<MockGroupClient>();
+    auto client = std::make_shared<test::MockGroupClient>();
     auto graph = ComputingGraph::make();
 
     auto x = opr::Host2DeviceCopy::make(*graph, host_x, cn0);
@@ -90,7 +53,7 @@ TEST(TestOprIORemote, IdentityMultiThread) {
     HostTensorGenerator<> gen;
     auto host_x = gen({2, 3}, cns[1]);
     HostTensorND host_x_get;
-    auto client = std::make_shared<MockGroupClient>();
+    auto client = std::make_shared<test::MockGroupClient>();
 
     auto sender = [&]() {
         auto graph = ComputingGraph::make();
@@ -123,7 +86,7 @@ TEST(TestOprIORemote, IdentityWithGopt) {
     HostTensorGenerator<> gen;
     auto host_x = gen({2, 3}, cns[0]);
     HostTensorND host_x_get;
-    auto client = std::make_shared<MockGroupClient>();
+    auto client = std::make_shared<test::MockGroupClient>();
 
     auto sender = [&]() {
         sys::set_thread_name("sender");
@@ -157,7 +120,7 @@ TEST(TestOprIORemote, APlusB) {
     HostTensorGenerator<> gen;
     auto host_x = gen({5, 7}, cns[0]), host_y = gen({5, 1}, cns[0]);
     HostTensorND host_z;
-    auto client = std::make_shared<MockGroupClient>();
+    auto client = std::make_shared<test::MockGroupClient>();
 
     auto sender = [&]() {
         auto graph = ComputingGraph::make();
@@ -208,7 +171,7 @@ TEST(TestOprIORemote, SendGrad) {
     HostTensorGenerator<> gen;
     auto host_x = gen({2, 3}, cns[0]);
     HostTensorND host_gx, host_loss;
-    auto client = std::make_shared<MockGroupClient>();
+    auto client = std::make_shared<test::MockGroupClient>();
 
     auto sender = [&]() {
         sys::set_thread_name("sender");

src/opr-mm/test/mock_client.h

@@ -0,0 +1,62 @@
+/**
+ * \file src/opr-mm/test/mock_client.cpp
+ * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+ *
+ * Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ */
+
+#include "megbrain/opr/group_manager.h"
+
+namespace mgb {
+namespace test {
+
+class MockGroupClient final : public opr::GroupClient {
+    public:
+        using RegisterInfo = opr::GroupManager::RegisterInfo;
+
+        MockGroupClient(const std::string& server_addr = "mock_addr") :
+            m_addr(server_addr) {
+        }
+
+        ~MockGroupClient() override = default;
+
+        const std::string& get_addr() const {
+            return m_addr;
+        }
+
+	RegisterInfo opr_register(const std::string& key, size_t nr_devices,
+                                  bool is_root, int rank, uint64_t comp_node_hash) {
+            return m_mgr.opr_register(key, nr_devices, is_root, rank, comp_node_hash);
+        }
+
+        std::vector<std::string> gather_uid(const std::string& uid,
+                const std::string& key, uint32_t size, uint32_t rank) {
+            return m_mgr.gather_uid(uid, key, size, rank);
+        }
+
+        void set_output_shape(const std::string& key,
+                              const TensorShape& shape) override {
+            m_mgr.set_output_shape(key, shape);
+        }
+
+        TensorShape get_output_shape(const std::string& key) override {
+            return m_mgr.get_output_shape(key);
+        }
+
+        uint32_t group_barrier(uint32_t size, uint32_t rank) override {
+            return m_mgr.group_barrier(size, rank);
+        }
+
+    private:
+        const std::string m_addr;
+        opr::GroupManager m_mgr;
+};
+
+} // namespace test
+} // namespace mgb
+
+// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}}